CUED undergraduate teaching site

Engineering Tripos Part IIB, 4G3: Computational Neuroscience, 2018-19

Module Leader

Prof Máté Lengyel

Lecturer

Prof Máté Lengyel, Dr Guillaume Hennequin, Dr Timothy O'Leary

Timing and Structure

Lent term. 16 lectures. Assessment: 100% coursework

Prerequisites

3G2 and 3G3 is useful but not essential

Aims

The aims of the course are to:

introduce alternative ways of modelling single neurons, and the way these single neuron models can be integrated into models of neural networks.
describe the challenges posed by neural coding and decoding, and the computational methods that can be applied to study them.
demonstrate case studies of computational functions that neural networks can implement.
describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system.
consider control tasks (sensorimotor and other) faced and solved by the nervous system.
examine the energy efficiency of neural computations.

Objectives

As specific objectives, by the end of the course students should be able to:

understand how neurons, and networks of neurons can be modelled in a biomimetic way, and how a systematic simplification of these models can be used to gain deeper insight into them.
develop an overview of how certain computational problems can be mapped onto neural architectures that solve them.
recognise the essential role of learning is the organisation of biological nervous systems.
appreciate the ways in which the nervous system is different from man-made intelligent systems, and their implications for engineering as well as neuroscience.

Content

The course covers basic topics in computational neuroscience, and demonstrates how mathematical analysis and ideas from dynamical systems, machine learning, optimal control, and probabilistic inference can be applied to gain insight into the workings of biological nervous systems. The course also highlights a number of real-world computational problems that need to be tackled by any ‘intelligent’ system, as well as the solutions that biology offers to some of these problems.

Principles of Computational Neuroscience (8L, M Lengyel)

how is neural activity generated? mechanistic neuron models
how to predict neural activity? descriptive neuron models
what should neurons do? normative neuron models
how to read neural activity? neural decoding
what happens when many neurons are connected? neural networks
how to tell a neural network what to do? supervised learning
how can neuronal networks learn without being told what to do? unsupervised learning
how do neural networks remember? auto-associative memory
how can our brains achieve the goal of life? reinforcement learning

Network dynamics & Plasticity (4L, G Hennequin)

linear and non-linear network dynamics
Hebbian plasticity
spike timing-dependant plasticity
learning receptive fields

Biophysics (2L, T O'Leary)

energetics of information processing
the energetic cost of spikes and synapses

Further notes

See the Moodle page for the course for more information (e.g. handouts, coursework assignments).

Examples papers

N/A

Coursework

Coursework	Format	Due date & marks
Coursework activity #1: reinforcement and representational learning Organisms learn about their environment to build internal representations that allow them to choose actions adaptively so as to maximise future reward. In this coursework, you will build simple models of reinforcement and representational learning and understand how they map onto neural phenomena. Learning objective: understand and implement the algorithm of temporal difference learning learn to interpret the predictions of temporal difference learning for dopaminergic midbrain activity understand the outputs of a simple independent components analysis (ICA) model and their relation to natural image statistics implement a simple divisive normalisation model and interpret its relation to natural image statistics as well as to activity in primary visual cortex (V1)	Individual report Anonymously marked	Posted Wed week 3 Due Wed week 5 [30/60]
Coursework activity #2: network dynamics and plasticity Most computations in the brain are implemented in networks of recurrently coupled neurons. In this coursework you will build simple neural network models and understand how they give rise to emergent dynamical and computational properties. Learning objective: implement simple neural networks and understand the effects of eigenvalues and eigenvectors on the resulting dynamics implement balanced neural circuits and understand how asynchronous and irregular activity is generated implement an associative memory network and understand how different parameters influence its memory capacity	Individual Report Anonymously marked	Posted Wed week 8 Due Wed two weeks later [30/60]

Format

Due date

& marks

Coursework activity #1: reinforcement and representational learning

Organisms learn about their environment to build internal representations that allow them to choose actions adaptively so as to maximise future reward. In this coursework, you will build simple models of reinforcement and representational learning and understand how they map onto neural phenomena.

Learning objective:

understand and implement the algorithm of temporal difference learning
learn to interpret the predictions of temporal difference learning for dopaminergic midbrain activity
understand the outputs of a simple independent components analysis (ICA) model and their relation to natural image statistics
implement a simple divisive normalisation model and interpret its relation to natural image statistics as well as to activity in primary visual cortex (V1)

Individual report

Anonymously marked

Posted Wed week 3

Due Wed week 5

[30/60]

Coursework activity #2: network dynamics and plasticity

Most computations in the brain are implemented in networks of recurrently coupled neurons. In this coursework you will build simple neural network models and understand how they give rise to emergent dynamical and computational properties.

Learning objective:

implement simple neural networks and understand the effects of eigenvalues and eigenvectors on the resulting dynamics
implement balanced neural circuits and understand how asynchronous and irregular activity is generated
implement an associative memory network and understand how different parameters influence its memory capacity

Individual Report

Anonymously marked

Posted Wed week 8

Due Wed two weeks later

[30/60]

See the Moodle page for the course for more information (e.g. handouts, coursework assignments).

Booklists

Please see the Booklist for Group G Courses for references for this module.

Examination Guidelines

Please refer to Form & conduct of the examinations.

Last modified: 17/05/2018 14:26

Engineering Tripos Part IIB, 4G3: Computational Neuroscience, 2017-18

Module Leader

Prof Máté Lengyel

Lecturer

Prof Máté Lengyel, Dr Guillaume Hennequin, Dr Timothy O'Leary

Timing and Structure

Lent term. 16 lectures. Assessment: 100% coursework

Prerequisites

3G2 and 3G3 is useful but not essential

Aims

The aims of the course are to:

introduce alternative ways of modelling single neurons, and the way these single neuron models can be integrated into models of neural networks.
describe the challenges posed by neural coding and decoding, and the computational methods that can be applied to study them.
demonstrate case studies of computational functions that neural networks can implement.
describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system.
consider control tasks (sensorimotor and other) faced and solved by the nervous system.
examine the energy efficiency of neural computations.

Objectives

As specific objectives, by the end of the course students should be able to:

understand how neurons, and networks of neurons can be modelled in a biomimetic way, and how a systematic simplification of these models can be used to gain deeper insight into them.
develop an overview of how certain computational problems can be mapped onto neural architectures that solve them.
recognise the essential role of learning is the organisation of biological nervous systems.
appreciate the ways in which the nervous system is different from man-made intelligent systems, and their implications for engineering as well as neuroscience.

Content

The course covers basic topics in computational neuroscience, and demonstrates how mathematical analysis and ideas from dynamical systems, machine learning, optimal control, and probabilistic inference can be applied to gain insight into the workings of biological nervous systems. The course also highlights a number of real-world computational problems that need to be tackled by any ‘intelligent’ system, as well as the solutions that biology offers to some of these problems.

Principles of Computational Neuroscience (8L, M Lengyel)

how is neural activity generated? mechanistic neuron models
how to predict neural activity? descriptive neuron models
what should neurons do? normative neuron models
how to read neural activity? neural decoding
what happens when many neurons are connected? neural networks
how to tell a neural network what to do? supervised learning
how can neuronal networks learn without being told what to do? unsupervised learning
how do neural networks remember? auto-associative memory
how can our brains achieve the goal of life? reinforcement learning

Network dynamics & Plasticity (4L, G Hennequin)

linear and non-linear network dynamics
Hebbian plasticity
spike timing-dependant plasticity
learning receptive fields

Biophysics (2L, T O'Leary)

energetics of information processing
the energetic cost of spikes and synapses

Further notes

See the Moodle page for the course for more information (e.g. handouts, coursework assignments).

Examples papers

N/A

Coursework

Coursework	Format	Due date & marks
Coursework activity #1: reinforcement and representational learning Organisms learn about their environment to build internal representations that allow them to choose actions adaptively so as to maximise future reward. In this coursework, you will build simple models of reinforcement and representational learning and understand how they map onto neural phenomena. Learning objective: understand and implement the algorithm of temporal difference learning learn to interpret the predictions of temporal difference learning for dopaminergic midbrain activity understand the outputs of a simple independent components analysis (ICA) model and their relation to natural image statistics implement a simple divisive normalisation model and interpret its relation to natural image statistics as well as to activity in primary visual cortex (V1)	Individual report Anonymously marked	Posted Wed week 3 Due Wed week 5 [30/60]
Coursework activity #2: network dynamics and plasticity Most computations in the brain are implemented in networks of recurrently coupled neurons. In this coursework you will build simple neural network models and understand how they give rise to emergent dynamical and computational properties. Learning objective: implement simple neural networks and understand the effects of eigenvalues and eigenvectors on the resulting dynamics implement balanced neural circuits and understand how asynchronous and irregular activity is generated implement an associative memory network and understand how different parameters influence its memory capacity	Individual Report Anonymously marked	Posted Wed week 8 Due Wed two weeks later [30/60]

Format

Due date

& marks

Coursework activity #1: reinforcement and representational learning

Organisms learn about their environment to build internal representations that allow them to choose actions adaptively so as to maximise future reward. In this coursework, you will build simple models of reinforcement and representational learning and understand how they map onto neural phenomena.

Learning objective:

understand and implement the algorithm of temporal difference learning
learn to interpret the predictions of temporal difference learning for dopaminergic midbrain activity
understand the outputs of a simple independent components analysis (ICA) model and their relation to natural image statistics
implement a simple divisive normalisation model and interpret its relation to natural image statistics as well as to activity in primary visual cortex (V1)

Individual report

Anonymously marked

Posted Wed week 3

Due Wed week 5

[30/60]

Coursework activity #2: network dynamics and plasticity

Most computations in the brain are implemented in networks of recurrently coupled neurons. In this coursework you will build simple neural network models and understand how they give rise to emergent dynamical and computational properties.

Learning objective:

implement simple neural networks and understand the effects of eigenvalues and eigenvectors on the resulting dynamics
implement balanced neural circuits and understand how asynchronous and irregular activity is generated
implement an associative memory network and understand how different parameters influence its memory capacity

Individual Report

Anonymously marked

Posted Wed week 8

Due Wed two weeks later

[30/60]

See the Moodle page for the course for more information (e.g. handouts, coursework assignments).

Booklists

Please see the Booklist for Group G Courses for references for this module.

Examination Guidelines

Please refer to Form & conduct of the examinations.

Last modified: 23/01/2018 18:06

Engineering Tripos Part IIB, 4G2: Bioelectronics, 2025-26

Module Leader

Prof George Malliaras

Lecturers

Prof George Malliaras

Timing and Structure

Michaelmas term. Lectures and coursework. Assessment: 100% coursework.

Aims

The aims of the course are to:

To provide an introduction to the field of bioelectronics.
To highlight the application of bioelectronic devices in the medical and consumer sectors.

Objectives

As specific objectives, by the end of the course students should be able to:

Extend principles of engineering to the development of bioelectronic devices.
Understand the principles of signal transduction between biology and electronics.
Appreciate the basic configuration and distinction among bioelectronic devices.
Demonstrate appreciation for the technical limits of performance.
Make design and selection decisions in response to measurement and actuation problems amenable to the use of bioelectronic devices.
Be able to evaluate novel trends in the field.

Content

One of the most important scientific and technological frontiers of our time is the interfacing of electronics with living systems. This endeavour promises to help gain a better understanding of biological phenomena and devliver new tools for diagnosis and treatment of pathologies including epilepsy and Partinson's disease. The aim of this course is to provide an introduction to the field of bioelectronics. The course will link science and engineering concepts to the principles, technologies, and applications of bioelectronics. The fundamentals of electrophysiology and electrochemistry will be applied to implantable and cutaneous bioelectronic devices and to in vitro systems to explain the principles of operation. Examples from current scientific literature will be analysed.

COURSE CONTENT

1. Introduction

Drivers for bioelectronics
What is bioelectronics?
Organisation of the module

Part I: Fundamentals

2. Elements of anatomy and function

The nervous system
The neuron
Neural circuits
Other systems of interest

3. Signal transduction across the biotic/abiotic interface

Types of electrodes
Electrochemical impedance
Electrochemical reactions
Neural recording and stimulation
Transistors as transducers
Complete systems

Part II: Technology

4. Implantable devices

Cardiac pacemaker
Auditory and visual prostheses
CNS and PNS implants
Implantable sensors and drug delivery systems
The foreign body response

5. Cutaneous devices

Recording devices for brain, heart, muscle
Stimulation devices for brain, heart, muscle
Wearable electronics and electronic skins

6. In vitro devices

Electrochemical biosensors
In vitro electrophysiology
Impedance biosensors
Body-on-a-chip

Part III: Translation and ethics

7. Translation

From the drawing board to patients at scale
Device discovery
Preclinical research and prototyping
Pathway to approval
Regulatory review
Post-market monitoring

8. Ethics

Medical ethics
When a device becomes part of you
What happens to the data?
Animal research

Further notes

The course will be interdispersed with discussions highlighting the state-of-the art in the field.

Coursework

The coursework will be assessed on two marked assignments. The first assignment will involve a laboratory session illustrating the functional demonstration of glucose sensor technology. The second assignment will involve a laboratory session illustrating the principle of a quartz crystal microbalance and related acoustic sensor technologies.

Coursework	Format	Due date & marks
Coursework activity #1 : Cutaneous electrophysiology Learning objectives: To introduce students to sensors employed for the measurement of electrophysiology. To explore different recording configurations. To quantitatively analyse measurements conducted using cutaneous electrodes. To extend the principles to the design of a sensor for the measurement of biopotentials.	Individual Report anonymously marked	Typically week 5 [30/60]
Coursework activity #2 : Mock design of a bioelectronic system Learning objectives: To give stduents a holistic view of bioelectronic system design. To explore different stimulation protocols used in neuromodulation. To explore different materials involved in the design of electrodes. To understand the process of translation.	Individual Report anonymously marked	Typically week 9 [30/60]

Coursework

Format

Due date

& marks

Coursework activity #1 : Cutaneous electrophysiology

Learning objectives:

To introduce students to sensors employed for the measurement of electrophysiology.
To explore different recording configurations.
To quantitatively analyse measurements conducted using cutaneous electrodes.
To extend the principles to the design of a sensor for the measurement of biopotentials.

Individual Report

anonymously marked

Typically week 5

[30/60]

Coursework activity #2 : Mock design of a bioelectronic system

Learning objectives:

To give stduents a holistic view of bioelectronic system design.
To explore different stimulation protocols used in neuromodulation.
To explore different materials involved in the design of electrodes.
To understand the process of translation.

Individual Report

anonymously marked

Typically week 9

[30/60]

Booklists

Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

UK-SPEC

This syllabus contributes to the following areas of the UK-SPEC standard:

Toggle display of UK-SPEC areas.

GT1

Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.

IA1

Apply appropriate quantitative science and engineering tools to the analysis of problems.

IA2

Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.

KU1

Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.

KU2

Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.

D1

Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.

D4

Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.

E1

Ability to use fundamental knowledge to investigate new and emerging technologies.

E2

Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.

P1

A thorough understanding of current practice and its limitations and some appreciation of likely new developments.

P3

Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).

US1

A comprehensive understanding of the scientific principles of own specialisation and related disciplines.

US3

An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.

US4

An awareness of developing technologies related to own specialisation.

Last modified: 22/07/2025 21:51

Engineering Tripos Part IIB, 4G2: Biosensors, 2018-19

Leader

Prof A Seshia

Lecturers

Prof A Seshia and Professor E A Hall

Timing and Structure

Lent term. Lectures and coursework. Assessment: 100% coursework.

Aims

The aims of the course are to:

link engineering principles to understanding of biosystems in sensors and bioelectronics

Objectives

As specific objectives, by the end of the course students should be able to:

extend principles of engineering to the development of bioanalytical devices and the design of biosensors.
understand the principles of linking cell components and biological pathways with energy transduction, sensing and detection
appreciate the basic configuration and distinction among biosensor systems.
demonstrate appreciation for the technical limits of performance.
make design and selection decisions in response to measurement problems amenable to the use of biosensors.

Content

This course covers the principles, technologies, methods and applications of biosensors and bioinstrumentation. The objective of this course is to link engineering principles to understanding of biosystems in sensors and bioelectronics. It will provide the student with detail of methods and procedures used in the design, fabrication and application of biosensors and bioelectronic devices. The fundamentals of measurement science are applied to optical, electrochemical, mass, and pressure signal transduction. Upon successful completion of this course, students are expected to be able to explain biosensing and transduction techniques, as well as design and construct biosensor instrumentation.

Introduction

Overview of Biosensors
Fundamental elements of biosensor devices
Engineering sensor proteins

Electrochemical Biosensors

Electrochemical principles
Amperometric biosensors and charge transfer pathways in enzymes
Glucose biosensors
Engineering electrochemical biosensors

Optical Biosensors

Optics for biosensors
Attenuated total reflection systems

Acoustic Biosensors

Analytical models
Acoustic sensor formats
Quartz crystal microbalance

Micro- and Nano-technologies for biosensors

Microfluidic interfaces for biosensors
DNA and protein microarrays
Microfabricated PCR technology

Diagnostics for the real world

Communication and tracking in health monitoring
Detection in resource limited settings

Coursework

The coursework will be assessed on two marked assignments. The first assignment will involve a laboratory session illustrating the functional demonstration of glucose sensor technology. The second assignment will involve a laboratory session illustrating the principle of a quartz crystal microbalance and related acoustic sensor technologies.

Coursework	Format	Due date & marks
Coursework activity #1 Glucose biosensors Learning objectives: To introduce students to electrochemical sensors employed for the measurement of glucose; To quantitatively analyse measurements conducted using test strip glucose biosensors on a range of samples; To extend the principles to the design of a biosensor for the measurement of lactate.	Individual Report anonymously marked	Mon week 5 [30/60]
[Coursework activity #2 Quartz crystal microbalance] Learning objectives: To introduce experimental techniques associated with employing the quartz crystal microbalance as a sensor; To assess the validity of analytical models associated with the operation of a quartz crystal microbalance and comment on discrepancies between theory and experiment; To extend concepts covered in the lectures and the laboratory to the conceptual design of an integrated acoustic sensor platform for the rapid screening and detection of infectious agents.	Individual Report anonymously marked	Wed week 9 [30/60]

Coursework

Format

Due date

& marks

Coursework activity #1 Glucose biosensors

Learning objectives:

To introduce students to electrochemical sensors employed for the measurement of glucose;
To quantitatively analyse measurements conducted using test strip glucose biosensors on a range of samples;
To extend the principles to the design of a biosensor for the measurement of lactate.

Individual Report

anonymously marked

Mon week 5

[30/60]

[Coursework activity #2 Quartz crystal microbalance]

Learning objectives:

To introduce experimental techniques associated with employing the quartz crystal microbalance as a sensor;
To assess the validity of analytical models associated with the operation of a quartz crystal microbalance and comment on discrepancies between theory and experiment;
To extend concepts covered in the lectures and the laboratory to the conceptual design of an integrated acoustic sensor platform for the rapid screening and detection of infectious agents.

Individual Report

anonymously marked

Wed week 9

[30/60]

Booklists

Please see the Booklist for Group G Courses for references for this module.

Examination Guidelines

Please refer to Form & conduct of the examinations.

UK-SPEC

This syllabus contributes to the following areas of the UK-SPEC standard:

Toggle display of UK-SPEC areas.

GT1

Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.

IA1

Apply appropriate quantitative science and engineering tools to the analysis of problems.

IA2

Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.

KU1

Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.

KU2

Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.

D1

Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.

D4

Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.

E1

Ability to use fundamental knowledge to investigate new and emerging technologies.

E2

Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.

P1

A thorough understanding of current practice and its limitations and some appreciation of likely new developments.

P3

Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).

US1

A comprehensive understanding of the scientific principles of own specialisation and related disciplines.

US3

An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.

US4

An awareness of developing technologies related to own specialisation.

Last modified: 17/05/2018 14:26

Engineering Tripos Part IIB, 4G2: Biosensors, 2017-18

Leader

Prof A Seshia

Lecturers

Prof A Seshia and Professor E A Hall

Timing and Structure

Lent term. Lectures and coursework. Assessment: 100% coursework.

Aims

The aims of the course are to:

link engineering principles to understanding of biosystems in sensors and bioelectronics

Objectives

As specific objectives, by the end of the course students should be able to:

extend principles of engineering to the development of bioanalytical devices and the design of biosensors.
understand the principles of linking cell components and biological pathways with energy transduction, sensing and detection
appreciate the basic configuration and distinction among biosensor systems.
demonstrate appreciation for the technical limits of performance.
make design and selection decisions in response to measurement problems amenable to the use of biosensors.

Content

This course covers the principles, technologies, methods and applications of biosensors and bioinstrumentation. The objective of this course is to link engineering principles to understanding of biosystems in sensors and bioelectronics. It will provide the student with detail of methods and procedures used in the design, fabrication and application of biosensors and bioelectronic devices. The fundamentals of measurement science are applied to optical, electrochemical, mass, and pressure signal transduction. Upon successful completion of this course, students are expected to be able to explain biosensing and transduction techniques, as well as design and construct biosensor instrumentation.

Introduction

Overview of Biosensors
Fundamental elements of biosensor devices
Engineering sensor proteins

Electrochemical Biosensors

Electrochemical principles
Amperometric biosensors and charge transfer pathways in enzymes
Glucose biosensors
Engineering electrochemical biosensors

Optical Biosensors

Optics for biosensors
Attenuated total reflection systems

Acoustic Biosensors

Analytical models
Acoustic sensor formats
Quartz crystal microbalance

Micro- and Nano-technologies for biosensors

Microfluidic interfaces for biosensors
DNA and protein microarrays
Microfabricated PCR technology

Diagnostics for the real world

Communication and tracking in health monitoring
Detection in resource limited settings

Coursework

The coursework will be assessed on two marked assignments. The first assignment will involve a laboratory session illustrating the functional demonstration of glucose sensor technology. The second assignment will involve a laboratory session illustrating the principle of a quartz crystal microbalance and related acoustic sensor technologies.

Coursework	Format	Due date & marks
Coursework activity #1 Glucose biosensors Learning objectives: To introduce students to electrochemical sensors employed for the measurement of glucose; To quantitatively analyse measurements conducted using test strip glucose biosensors on a range of samples; To extend the principles to the design of a biosensor for the measurement of lactate.	Individual Report anonymously marked	Mon week 5 [30/60]
[Coursework activity #2 Quartz crystal microbalance] Learning objectives: To introduce experimental techniques associated with employing the quartz crystal microbalance as a sensor; To assess the validity of analytical models associated with the operation of a quartz crystal microbalance and comment on discrepancies between theory and experiment; To extend concepts covered in the lectures and the laboratory to the conceptual design of an integrated acoustic sensor platform for the rapid screening and detection of infectious agents.	Individual Report anonymously marked	Wed week 9 [30/60]

Coursework

Format

Due date

& marks

Coursework activity #1 Glucose biosensors

Learning objectives:

To introduce students to electrochemical sensors employed for the measurement of glucose;
To quantitatively analyse measurements conducted using test strip glucose biosensors on a range of samples;
To extend the principles to the design of a biosensor for the measurement of lactate.

Individual Report

anonymously marked

Mon week 5

[30/60]

[Coursework activity #2 Quartz crystal microbalance]

Learning objectives:

To introduce experimental techniques associated with employing the quartz crystal microbalance as a sensor;
To assess the validity of analytical models associated with the operation of a quartz crystal microbalance and comment on discrepancies between theory and experiment;
To extend concepts covered in the lectures and the laboratory to the conceptual design of an integrated acoustic sensor platform for the rapid screening and detection of infectious agents.

Individual Report

anonymously marked

Wed week 9

[30/60]

Booklists

Please see the Booklist for Group G Courses for references for this module.

Examination Guidelines

Please refer to Form & conduct of the examinations.

UK-SPEC

This syllabus contributes to the following areas of the UK-SPEC standard:

Toggle display of UK-SPEC areas.

GT1

Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.

IA1

Apply appropriate quantitative science and engineering tools to the analysis of problems.

IA2

Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.

KU1

Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.

KU2

Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.

D1

Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.

D4

Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.

E1

Ability to use fundamental knowledge to investigate new and emerging technologies.

E2

Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.

P1

A thorough understanding of current practice and its limitations and some appreciation of likely new developments.

P3

Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).

US1

A comprehensive understanding of the scientific principles of own specialisation and related disciplines.

US3

An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.

US4

An awareness of developing technologies related to own specialisation.

Last modified: 05/10/2017 11:12

Engineering Tripos Part IIB, 4F13: Probabilistic Machine Learning, 2025-26

Module Leader

Dr H Ge

Lecturers

Dr H Ge, Dr A Tewari, Dr G Cantwell

Timing and Structure

Michaelmas term. 14 lectures + 2 examples classes. Assessment: 100% coursework

Prerequisites

3F3 useful

Aims

The aims of the course are to:

introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning.

Objectives

As specific objectives, by the end of the course students should be able to:

demonstrate a good understanding of basic concepts in statistical machine learning.
apply basic ML methods to practical problems.

Content

Machine learning (ML) is an interdisciplinary field focusing on both the mathematical foundations and practical applications of systems that learn, reason and act. The goal of machine learning is to automatically extract knowledge from observed data for the purposes of making predictions, decisions and understanding the world.

The aim of this module is to introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning. The module will be structured around three recent illustrative successful applications: Gaussian processes for regression and classification, Latent Dirichlet Allocation models for unsupervised text modelling and the TrueSkill probabilistic ranking model.

Linear models, maximum likelihood and Bayesian inference
Gaussian distribution and Gaussian process
Model selection
The Expectation Propagation (EP) algorithm
Latent variable models
The Expectation Maximization (EM) algorithm
Dirichlet Distribution and Dirichlet Process
Variational inference
Generative models, graphical models: Factor graphs

Lectures will be supported by Octave/MATLAB demonstrations.

A detailed syllabus and information about the coursework is available on the moodle website: https://www.vle.cam.ac.uk/course/view.php?id=69021

Coursework

Coursework	Format	Due date & marks
[Coursework activity #1 Gaussian Processes] Coursework 1 brief description Learning objective: To gain experience in Bayesian Gaussian Process (GP) regression. To familiarise yourself with the GPML toolbox. To understand properties of covariance functions. To perform hyperparameter learning. To understand how model selection can be done using the marginal likelihood.	Individual/group Report / Presentation anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	day during term, ex: Fri week 5 [20/60]
[Coursework activity #2 Probabilistic Ranking] Coursework 2 brief description Learning objective: To understand inference in continuous probabilistic models represented as factor graphs. To understand the Gibbs sampling algorithm and gain experience with using Markov chain Monte Carlo (MCMC) for inference. To understand message passing on (loopy) factor graphs. To learn how to construct approximate messages using Expectation Propagation (EP).	Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	Fri week 7 [20/60]
[Coursework activity #3 Latent Dirichlet Allocation models for documents] Coursework 3 brief description Learning objective: To understand unsupervised learnign in discrete graphical models for documents. To develop an understanding of graphical models with more complex latent structure. To understnad and apply the Expectation Maximization (EM) and Gibbs sampling algorithms. To perform unsupervised learning using Latent Dirichlet Allocation model on a collection of documents.	Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	Fri week 9 [20/60]

Format

Due date

& marks

[Coursework activity #1 Gaussian Processes]

Coursework 1 brief description

Learning objective:

To gain experience in Bayesian Gaussian Process (GP) regression.
To familiarise yourself with the GPML toolbox.
To understand properties of covariance functions.
To perform hyperparameter learning.
To understand how model selection can be done using the marginal likelihood.

Individual/group

Report / Presentation

anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

day during term, ex:

Fri week 5

[20/60]

[Coursework activity #2 Probabilistic Ranking]

Coursework 2 brief description

Learning objective:

To understand inference in continuous probabilistic models represented as factor graphs.
To understand the Gibbs sampling algorithm and gain experience with using Markov chain Monte Carlo (MCMC) for inference.
To understand message passing on (loopy) factor graphs.
To learn how to construct approximate messages using Expectation Propagation (EP).

Individual Report

Anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

Fri week 7

[20/60]

[Coursework activity #3 Latent Dirichlet Allocation models for documents]

Coursework 3 brief description

Learning objective:

To understand unsupervised learnign in discrete graphical models for documents.
To develop an understanding of graphical models with more complex latent structure.
To understnad and apply the Expectation Maximization (EM) and Gibbs sampling algorithms.
To perform unsupervised learning using Latent Dirichlet Allocation model on a collection of documents.

Individual Report

Anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

Fri week 9

[20/60]

Booklists

Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

UK-SPEC

This syllabus contributes to the following areas of the UK-SPEC standard:

Toggle display of UK-SPEC areas.

GT1

Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.

IA1

Apply appropriate quantitative science and engineering tools to the analysis of problems.

IA2

Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.

KU1

Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.

KU2

Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.

E1

Ability to use fundamental knowledge to investigate new and emerging technologies.

E2

Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.

E3

Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.

E4

Understanding of and ability to apply a systems approach to engineering problems.

P1

A thorough understanding of current practice and its limitations and some appreciation of likely new developments.

P3

Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).

P8

Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.

US1

A comprehensive understanding of the scientific principles of own specialisation and related disciplines.

US2

A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.

US3

An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.

Last modified: 04/06/2025 13:30

Engineering Tripos Part IIB, 4F13: Probabilistic Machine Learning, 2024-25

Module Leader

Prof C Rasmussen

Lecturers

Prof C Rasmussen

Timing and Structure

Michaelmas term. 14 lectures + 2 examples classes. Assessment: 100% coursework

Prerequisites

3F3 useful

Aims

The aims of the course are to:

introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning.

Objectives

As specific objectives, by the end of the course students should be able to:

demonstrate a good understanding of basic concepts in statistical machine learning.
apply basic ML methods to practical problems.

Content

Machine learning (ML) is an interdisciplinary field focusing on both the mathematical foundations and practical applications of systems that learn, reason and act. The goal of machine learning is to automatically extract knowledge from observed data for the purposes of making predictions, decisions and understanding the world.

The aim of this module is to introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning. The module will be structured around three recent illustrative successful applications: Gaussian processes for regression and classification, Latent Dirichlet Allocation models for unsupervised text modelling and the TrueSkill probabilistic ranking model.

Linear models, maximum likelihood and Bayesian inference
Gaussian distribution and Gaussian process
Model selection
The Expectation Propagation (EP) algorithm
Latent variable models
The Expectation Maximization (EM) algorithm
Dirichlet Distribution and Dirichlet Process
Variational inference
Generative models, graphical models: Factor graphs

Lectures will be supported by Octave/MATLAB demonstrations.

A detailed syllabus and information about the coursework is available on the moodle website: https://www.vle.cam.ac.uk/course/view.php?id=69021

Coursework

Coursework	Format	Due date & marks
[Coursework activity #1 Gaussian Processes] Coursework 1 brief description Learning objective: To gain experience in Bayesian Gaussian Process (GP) regression. To familiarise yourself with the GPML toolbox. To understand properties of covariance functions. To perform hyperparameter learning. To understand how model selection can be done using the marginal likelihood.	Individual/group Report / Presentation anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	day during term, ex: Fri week 5 [20/60]
[Coursework activity #2 Probabilistic Ranking] Coursework 2 brief description Learning objective: To understand inference in continuous probabilistic models represented as factor graphs. To understand the Gibbs sampling algorithm and gain experience with using Markov chain Monte Carlo (MCMC) for inference. To understand message passing on (loopy) factor graphs. To learn how to construct approximate messages using Expectation Propagation (EP).	Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	Fri week 7 [20/60]
[Coursework activity #3 Latent Dirichlet Allocation models for documents] Coursework 3 brief description Learning objective: To understand unsupervised learnign in discrete graphical models for documents. To develop an understanding of graphical models with more complex latent structure. To understnad and apply the Expectation Maximization (EM) and Gibbs sampling algorithms. To perform unsupervised learning using Latent Dirichlet Allocation model on a collection of documents.	Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	Fri week 9 [20/60]

Format

Due date

& marks

[Coursework activity #1 Gaussian Processes]

Coursework 1 brief description

Learning objective:

To gain experience in Bayesian Gaussian Process (GP) regression.
To familiarise yourself with the GPML toolbox.
To understand properties of covariance functions.
To perform hyperparameter learning.
To understand how model selection can be done using the marginal likelihood.

Individual/group

Report / Presentation

anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

day during term, ex:

Fri week 5

[20/60]

[Coursework activity #2 Probabilistic Ranking]

Coursework 2 brief description

Learning objective:

To understand inference in continuous probabilistic models represented as factor graphs.
To understand the Gibbs sampling algorithm and gain experience with using Markov chain Monte Carlo (MCMC) for inference.
To understand message passing on (loopy) factor graphs.
To learn how to construct approximate messages using Expectation Propagation (EP).

Individual Report

Anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

Fri week 7

[20/60]

[Coursework activity #3 Latent Dirichlet Allocation models for documents]

Coursework 3 brief description

Learning objective:

To understand unsupervised learnign in discrete graphical models for documents.
To develop an understanding of graphical models with more complex latent structure.
To understnad and apply the Expectation Maximization (EM) and Gibbs sampling algorithms.
To perform unsupervised learning using Latent Dirichlet Allocation model on a collection of documents.

Individual Report

Anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

Fri week 9

[20/60]

Booklists

Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

UK-SPEC

This syllabus contributes to the following areas of the UK-SPEC standard:

Toggle display of UK-SPEC areas.

GT1

Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.

IA1

Apply appropriate quantitative science and engineering tools to the analysis of problems.

IA2

Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.

KU1

Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.

KU2

Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.

E1

Ability to use fundamental knowledge to investigate new and emerging technologies.

E2

Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.

E3

Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.

E4

Understanding of and ability to apply a systems approach to engineering problems.

P1

A thorough understanding of current practice and its limitations and some appreciation of likely new developments.

P3

Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).

P8

Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.

US1

A comprehensive understanding of the scientific principles of own specialisation and related disciplines.

US2

A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.

US3

An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.

Last modified: 31/05/2024 10:08

Engineering Tripos Part IIB, 4F13: Probabilistic Machine Learning, 2023-24

Module Leader

Prof C Rasmussen

Lecturers

Prof C Rasmussen

Timing and Structure

Michaelmas term. 14 lectures + 2 examples classes. Assessment: 100% coursework

Prerequisites

3F3 useful

Aims

The aims of the course are to:

introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning.

Objectives

As specific objectives, by the end of the course students should be able to:

demonstrate a good understanding of basic concepts in statistical machine learning.
apply basic ML methods to practical problems.

Content

Machine learning (ML) is an interdisciplinary field focusing on both the mathematical foundations and practical applications of systems that learn, reason and act. The goal of machine learning is to automatically extract knowledge from observed data for the purposes of making predictions, decisions and understanding the world.

The aim of this module is to introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning. The module will be structured around three recent illustrative successful applications: Gaussian processes for regression and classification, Latent Dirichlet Allocation models for unsupervised text modelling and the TrueSkill probabilistic ranking model.

Linear models, maximum likelihood and Bayesian inference
Gaussian distribution and Gaussian process
Model selection
The Expectation Propagation (EP) algorithm
Latent variable models
The Expectation Maximization (EM) algorithm
Dirichlet Distribution and Dirichlet Process
Variational inference
Generative models, graphical models: Factor graphs

Lectures will be supported by Octave/MATLAB demonstrations.

A detailed syllabus and information about the coursework is available on the moodle website: https://www.vle.cam.ac.uk/course/view.php?id=69021

Coursework

Coursework	Format	Due date & marks
[Coursework activity #1 Gaussian Processes] Coursework 1 brief description Learning objective: To gain experience in Bayesian Gaussian Process (GP) regression. To familiarise yourself with the GPML toolbox. To understand properties of covariance functions. To perform hyperparameter learning. To understand how model selection can be done using the marginal likelihood.	Individual/group Report / Presentation anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	day during term, ex: Fri week 5 [20/60]
[Coursework activity #2 Probabilistic Ranking] Coursework 2 brief description Learning objective: To understand inference in continuous probabilistic models represented as factor graphs. To understand the Gibbs sampling algorithm and gain experience with using Markov chain Monte Carlo (MCMC) for inference. To understand message passing on (loopy) factor graphs. To learn how to construct approximate messages using Expectation Propagation (EP).	Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	Fri week 7 [20/60]
[Coursework activity #3 Latent Dirichlet Allocation models for documents] Coursework 3 brief description Learning objective: To understand unsupervised learnign in discrete graphical models for documents. To develop an understanding of graphical models with more complex latent structure. To understnad and apply the Expectation Maximization (EM) and Gibbs sampling algorithms. To perform unsupervised learning using Latent Dirichlet Allocation model on a collection of documents.	Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	Fri week 9 [20/60]

Format

Due date

& marks

[Coursework activity #1 Gaussian Processes]

Coursework 1 brief description

Learning objective:

To gain experience in Bayesian Gaussian Process (GP) regression.
To familiarise yourself with the GPML toolbox.
To understand properties of covariance functions.
To perform hyperparameter learning.
To understand how model selection can be done using the marginal likelihood.

Individual/group

Report / Presentation

anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

day during term, ex:

Fri week 5

[20/60]

[Coursework activity #2 Probabilistic Ranking]

Coursework 2 brief description

Learning objective:

To understand inference in continuous probabilistic models represented as factor graphs.
To understand the Gibbs sampling algorithm and gain experience with using Markov chain Monte Carlo (MCMC) for inference.
To understand message passing on (loopy) factor graphs.
To learn how to construct approximate messages using Expectation Propagation (EP).

Individual Report

Anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

Fri week 7

[20/60]

[Coursework activity #3 Latent Dirichlet Allocation models for documents]

Coursework 3 brief description

Learning objective:

To understand unsupervised learnign in discrete graphical models for documents.
To develop an understanding of graphical models with more complex latent structure.
To understnad and apply the Expectation Maximization (EM) and Gibbs sampling algorithms.
To perform unsupervised learning using Latent Dirichlet Allocation model on a collection of documents.

Individual Report

Anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

Fri week 9

[20/60]

Booklists

Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

UK-SPEC

This syllabus contributes to the following areas of the UK-SPEC standard:

Toggle display of UK-SPEC areas.

GT1

Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.

IA1

Apply appropriate quantitative science and engineering tools to the analysis of problems.

IA2

Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.

KU1

Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.

KU2

Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.

E1

Ability to use fundamental knowledge to investigate new and emerging technologies.

E2

Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.

E3

Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.

E4

Understanding of and ability to apply a systems approach to engineering problems.

P1

A thorough understanding of current practice and its limitations and some appreciation of likely new developments.

P3

Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).

P8

Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.

US1

A comprehensive understanding of the scientific principles of own specialisation and related disciplines.

US2

A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.

US3

An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.

Last modified: 30/05/2023 15:31

Engineering Tripos Part IIB, 4F13: Probabilistic Machine Learning, 2022-23

Module Leader

Prof C Rasmussen

Lecturers

Prof C Rasmussen, Dr D Krueger

Timing and Structure

Michaelmas term. 14 lectures + 2 examples classes. Assessment: 100% coursework

Prerequisites

3F3 useful

Aims

The aims of the course are to:

introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning.

Objectives

As specific objectives, by the end of the course students should be able to:

demonstrate a good understanding of basic concepts in statistical machine learning.
apply basic ML methods to practical problems.

Content

Machine learning (ML) is an interdisciplinary field focusing on both the mathematical foundations and practical applications of systems that learn, reason and act. The goal of machine learning is to automatically extract knowledge from observed data for the purposes of making predictions, decisions and understanding the world.

The aim of this module is to introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning. The module will be structured around three recent illustrative successful applications: Gaussian processes for regression and classification, Latent Dirichlet Allocation models for unsupervised text modelling and the TrueSkill probabilistic ranking model.

Linear models, maximum likelihood and Bayesian inference
Gaussian distribution and Gaussian process
Model selection
The Expectation Propagation (EP) algorithm
Latent variable models
The Expectation Maximization (EM) algorithm
Dirichlet Distribution and Dirichlet Process
Variational inference
Generative models, graphical models: Factor graphs

Lectures will be supported by Octave/MATLAB demonstrations.

A detailed syllabus and information about the coursework is available on the moodle website: https://www.vle.cam.ac.uk/course/view.php?id=69021

Coursework

Coursework	Format	Due date & marks
[Coursework activity #1 Gaussian Processes] Coursework 1 brief description Learning objective: To gain experience in Bayesian Gaussian Process (GP) regression. To familiarise yourself with the GPML toolbox. To understand properties of covariance functions. To perform hyperparameter learning. To understand how model selection can be done using the marginal likelihood.	Individual/group Report / Presentation anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	day during term, ex: Fri week 5 [20/60]
[Coursework activity #2 Probabilistic Ranking] Coursework 2 brief description Learning objective: To understand inference in continuous probabilistic models represented as factor graphs. To understand the Gibbs sampling algorithm and gain experience with using Markov chain Monte Carlo (MCMC) for inference. To understand message passing on (loopy) factor graphs. To learn how to construct approximate messages using Expectation Propagation (EP).	Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	Fri week 7 [20/60]
[Coursework activity #3 Latent Dirichlet Allocation models for documents] Coursework 3 brief description Learning objective: To understand unsupervised learnign in discrete graphical models for documents. To develop an understanding of graphical models with more complex latent structure. To understnad and apply the Expectation Maximization (EM) and Gibbs sampling algorithms. To perform unsupervised learning using Latent Dirichlet Allocation model on a collection of documents.	Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	Fri week 9 [20/60]

Format

Due date

& marks

[Coursework activity #1 Gaussian Processes]

Coursework 1 brief description

Learning objective:

To gain experience in Bayesian Gaussian Process (GP) regression.
To familiarise yourself with the GPML toolbox.
To understand properties of covariance functions.
To perform hyperparameter learning.
To understand how model selection can be done using the marginal likelihood.

Individual/group

Report / Presentation

anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

day during term, ex:

Fri week 5

[20/60]

[Coursework activity #2 Probabilistic Ranking]

Coursework 2 brief description

Learning objective:

To understand inference in continuous probabilistic models represented as factor graphs.
To understand the Gibbs sampling algorithm and gain experience with using Markov chain Monte Carlo (MCMC) for inference.
To understand message passing on (loopy) factor graphs.
To learn how to construct approximate messages using Expectation Propagation (EP).

Individual Report

Anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

Fri week 7

[20/60]

[Coursework activity #3 Latent Dirichlet Allocation models for documents]

Coursework 3 brief description

Learning objective:

To understand unsupervised learnign in discrete graphical models for documents.
To develop an understanding of graphical models with more complex latent structure.
To understnad and apply the Expectation Maximization (EM) and Gibbs sampling algorithms.
To perform unsupervised learning using Latent Dirichlet Allocation model on a collection of documents.

Individual Report

Anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

Fri week 9

[20/60]

Booklists

Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

UK-SPEC

This syllabus contributes to the following areas of the UK-SPEC standard:

Toggle display of UK-SPEC areas.

GT1

Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.

IA1

Apply appropriate quantitative science and engineering tools to the analysis of problems.

IA2

Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.

KU1

Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.

KU2

Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.

E1

Ability to use fundamental knowledge to investigate new and emerging technologies.

E2

Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.

E3

Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.

E4

Understanding of and ability to apply a systems approach to engineering problems.

P1

A thorough understanding of current practice and its limitations and some appreciation of likely new developments.

P3

Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).

P8

Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.

US1

A comprehensive understanding of the scientific principles of own specialisation and related disciplines.

US2

A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.

US3

An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.

Last modified: 24/05/2022 13:12

Engineering Tripos Part IIB, 4F13: Probabilistic Machine Learning, 2021-22

Module Leader

Prof C Rasmussen

Lecturers

Prof C Rasmussen, Dr D Krueger

Timing and Structure

Michaelmas term. 14 lectures + 2 examples classes. Assessment: 100% coursework

Prerequisites

3F3 useful

Aims

The aims of the course are to:

introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning.

Objectives

As specific objectives, by the end of the course students should be able to:

demonstrate a good understanding of basic concepts in statistical machine learning.
apply basic ML methods to practical problems.

Content

Machine learning (ML) is an interdisciplinary field focusing on both the mathematical foundations and practical applications of systems that learn, reason and act. The goal of machine learning is to automatically extract knowledge from observed data for the purposes of making predictions, decisions and understanding the world.

The aim of this module is to introduce students to basic concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning. The module will be structured around three recent illustrative successful applications: Gaussian processes for regression and classification, Latent Dirichlet Allocation models for unsupervised text modelling and the TrueSkill probabilistic ranking model.

Linear models, maximum likelihood and Bayesian inference
Gaussian distribution and Gaussian process
Model selection
The Expectation Propagation (EP) algorithm
Latent variable models
The Expectation Maximization (EM) algorithm
Dirichlet Distribution and Dirichlet Process
Variational inference
Generative models, graphical models: Factor graphs

Lectures will be supported by Octave/MATLAB demonstrations.

A detailed syllabus and information about the coursework is available on the moodle website: https://www.vle.cam.ac.uk/course/view.php?id=69021

Coursework

Coursework	Format	Due date & marks
[Coursework activity #1 Gaussian Processes] Coursework 1 brief description Learning objective: To gain experience in Bayesian Gaussian Process (GP) regression. To familiarise yourself with the GPML toolbox. To understand properties of covariance functions. To perform hyperparameter learning. To understand how model selection can be done using the marginal likelihood.	Individual/group Report / Presentation anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	day during term, ex: Fri week 5 [20/60]
[Coursework activity #2 Probabilistic Ranking] Coursework 2 brief description Learning objective: To understand inference in continuous probabilistic models represented as factor graphs. To understand the Gibbs sampling algorithm and gain experience with using Markov chain Monte Carlo (MCMC) for inference. To understand message passing on (loopy) factor graphs. To learn how to construct approximate messages using Expectation Propagation (EP).	Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	Fri week 7 [20/60]
[Coursework activity #3 Latent Dirichlet Allocation models for documents] Coursework 3 brief description Learning objective: To understand unsupervised learnign in discrete graphical models for documents. To develop an understanding of graphical models with more complex latent structure. To understnad and apply the Expectation Maximization (EM) and Gibbs sampling algorithms. To perform unsupervised learning using Latent Dirichlet Allocation model on a collection of documents.	Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs	Fri week 9 [20/60]

Format

Due date

& marks

[Coursework activity #1 Gaussian Processes]

Coursework 1 brief description

Learning objective:

To gain experience in Bayesian Gaussian Process (GP) regression.
To familiarise yourself with the GPML toolbox.
To understand properties of covariance functions.
To perform hyperparameter learning.
To understand how model selection can be done using the marginal likelihood.

Individual/group

Report / Presentation

anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

day during term, ex:

Fri week 5

[20/60]

[Coursework activity #2 Probabilistic Ranking]

Coursework 2 brief description

Learning objective:

To understand inference in continuous probabilistic models represented as factor graphs.
To understand the Gibbs sampling algorithm and gain experience with using Markov chain Monte Carlo (MCMC) for inference.
To understand message passing on (loopy) factor graphs.
To learn how to construct approximate messages using Expectation Propagation (EP).

Individual Report

Anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

Fri week 7

[20/60]

[Coursework activity #3 Latent Dirichlet Allocation models for documents]

Coursework 3 brief description

Learning objective:

To understand unsupervised learnign in discrete graphical models for documents.
To develop an understanding of graphical models with more complex latent structure.
To understnad and apply the Expectation Maximization (EM) and Gibbs sampling algorithms.
To perform unsupervised learning using Latent Dirichlet Allocation model on a collection of documents.

Individual Report

Anonymously marked for MPHIL/MLSALT & Undergraduates

Nonanonymously marked for PhDs

Fri week 9

[20/60]

Booklists

Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

UK-SPEC

This syllabus contributes to the following areas of the UK-SPEC standard:

Toggle display of UK-SPEC areas.

GT1

Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.

IA1

Apply appropriate quantitative science and engineering tools to the analysis of problems.

IA2

Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.

KU1

Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.

KU2

Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.

E1

Ability to use fundamental knowledge to investigate new and emerging technologies.

E2

Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.

E3

Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.

E4

Understanding of and ability to apply a systems approach to engineering problems.

P1

A thorough understanding of current practice and its limitations and some appreciation of likely new developments.

P3

Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).

P8

Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.

US1

A comprehensive understanding of the scientific principles of own specialisation and related disciplines.

US2

A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.

US3

An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.

Last modified: 20/05/2021 07:49

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Module Leader

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Prerequisites

Aims

Objectives

Content

Principles of Computational Neuroscience (8L, M Lengyel)

Network dynamics & Plasticity (4L, G Hennequin)

Biophysics (2L, T O'Leary)

Further notes

Examples papers

Coursework

Booklists

Examination Guidelines

Module Leader

Lecturer

Timing and Structure

Prerequisites

Aims

Objectives

Content

Principles of Computational Neuroscience (8L, M Lengyel)

Network dynamics & Plasticity (4L, G Hennequin)

Biophysics (2L, T O'Leary)

Further notes

Examples papers

Coursework

Booklists

Examination Guidelines

Module Leader

Lecturers

Timing and Structure

Aims

Objectives

Content

COURSE CONTENT

1. Introduction

Part I: Fundamentals

2. Elements of anatomy and function

3. Signal transduction across the biotic/abiotic interface

Part II: Technology

4. Implantable devices

5. Cutaneous devices

6. In vitro devices

Part III: Translation and ethics

7. Translation

8. Ethics

Further notes

Coursework

Booklists

Examination Guidelines

UK-SPEC

GT1

IA1

IA2

KU1

KU2

D1

D4

E1

E2

P1

P3

US1

US3

US4

Leader

Lecturers

Timing and Structure

Aims

Objectives

Content

Introduction

Electrochemical Biosensors

Optical Biosensors

Acoustic Biosensors

Micro- and Nano-technologies for biosensors

Diagnostics for the real world