Engineering Tripos Part IIB, 4G6: Cellular & Molecular Biomechanics, 2018-19
Module Leader
Lecturers
Prof V Deshpande and Prof N Fleck
Timing and Structure
Lent term. 14 lectures + 2 examples classes. Assessment: 100% exam
Prerequisites
3C7 useful.
Aims
The aims of the course are to:
- deal with the relation between microstructure of and properties such as strength, stiffness and actuation capability of natural materials such as cells and tissues and their properties, including stiffness.
Objectives
As specific objectives, by the end of the course students should be able to:
- understand the relation between micro-structure of soft biological materials and their mechanical properties.
- have a working understanding of the various components within plant and animal cells with a more detailed knowledge of the cytoskeletal components.
- understand the origins of the mechanical forces generated due to the polymerization of cytoskeletal proteins and derive the key equations.
- develop an understanding of muscles as actuators at the tissue, cell and protein length scales.
Content
Overview Lecture (Prof N. A. Fleck 1L)
The microstructure of the cell – animal cells, plant cells and the sub-cell building materials.
Mechanical Properties of Soft Solids (4L) (Prof. N A Fleck)
- The mechanical properties of natural materials – property maps
- Bending versus stretching micro-structures and entropic networks
- The notion of persistence length
- Models of stiffness and strength
- Mechanics of skin: stress v. strain responses, toughness and skin injection
The cytoskeleton (4L) (Prof.V. Deshpande)
- Review of basic thermodynamics and kinetics
- Introduction to cytoskeletal components and basics mechanics of the filaments
- Re-organization of the cytoskeletal filaments: polymerization, force generation and an introduction to motility
Muscle Mechanics (5L) (Prof.V. Deshpande)
- Twitch and tetanus and the Hill model
- Structure of the muscle: fibers, fibrils and contractile proteins
- Sources of energy in the muscle- Lohmann reaction
- Huxley Sliding filament model
- Models of myosin
Further notes
Further details and online resources:-
http://www-g.eng.cam.ac.uk/lifesciences/courses.html
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.
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.
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.
Last modified: 02/10/2018 13:47
Engineering Tripos Part IIB, 4G4: Biomimetics, 2017-18
Module Leader
Lecturers
Dr M Oyen, Dr F Iida, and Dr W Federle
Timing and Structure
Lent term. 12 lectures + Group project work. Assessment: 100% coursework
Aims
The aims of the course are to:
- Develop an understanding the ways engineers adopt and adapt ideas from nature and make new engineering entities.
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand how scientists are borrowing from nature across many different fields of engineering, with in-depth understanding on one topic (project)
- Identify new possibilities for biomimesis in design.
- Learn how to read the current biomimetics literature.
Content
Introduction and Project assignment ( M. Oyen, CUED) (2L)
Bioinspired Robotics (F. Iida, CUED) (2L)
- Legged robot locomotion and underactuated motion control
- Soft robotics and bio-inspired actuation
Biomimetic adhesion and adhesives (W. Federle, Zoology) (4L)
- Attachment devices and mechanisms in nature
- Approaches to develop biomimetic adhesives
Biomimetic materials (M. Oyen, CUED) (4L)
- Protein-based structural materials
- Protein folding, weak bonding, hydration
- Biomineralisation
- Biosilification, calcium carbonates, calcium phosphates
- Composite mechanics applied to natural materials
- Polymer amphiphiles
- Self-healing materials
Project Presentations (2L)
Coursework
Students will work in groups of 2-3 on a biomimetics design portfolio for one specific case from any of the following: biomimetic materials (e.g. bone, shell); natural structures (e.g. photonic crystals, lotus paint, adhesives); robots that swim, fly, or crawl like creatures; or any other biomimetics topic identified as acceptable via discussion with the module leader.
| Coursework | Format |
Due date & marks |
|---|---|---|
|
[Group Presentation] Comparison of natural vs engineering solutions to a specific problem Learning objective:
|
Group Presentation non-anonymously marked |
Week 8 Lent [12/60] |
|
[Preliminary Report] Comparison of natural vs engineering solutions to a specific problem Learning objective:
|
Individual Report non-anonymously marked |
Friday week 10 Lent [18/60] |
|
[Final Report] Biomimetic design dossier, written report plus additional drawings, calculations, computer simulations, and prototypes Learning objective:
|
Individual Report non-anonymously marked |
Tuesday week 1 of Easter Term [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.
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: 04/10/2017 13:59
Engineering Tripos Part IIB, 4G2: Biosensors, 2018-19
Leader
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:
|
Individual Report anonymously marked |
Mon week 5 [30/60] |
|
[Coursework activity #2 Quartz crystal microbalance] Learning objectives:
|
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: Bioelectronics, 2025-26
Module Leader
Lecturers
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:
|
Individual Report anonymously marked |
Typically week 5 [30/60] |
|
Coursework activity #2 : Mock design of a bioelectronic system Learning objectives:
|
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, 2017-18
Leader
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:
|
Individual Report anonymously marked |
Mon week 5 [30/60] |
|
[Coursework activity #2 Quartz crystal microbalance] Learning objectives:
|
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, 2017-18
Module Leader
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 course website: http://mlg.eng.cam.ac.uk/teaching/4f13/
Coursework
| Coursework | Format |
Due date & marks |
|---|---|---|
|
[Coursework activity #1 Gaussian Processes] Coursework 1 brief description Learning objective:
|
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:
|
Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs |
Fri week 7 [20/60] |
| [Coursework activity #3 Latent Dirichlet Allocation models or documents] Coursework 3 brief description Learning objective:
|
Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs |
Fri week 9 [20/60] |
Booklists
Please see the Booklist for Group F 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.
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: 17/01/2018 12:48
Engineering Tripos Part IIB, 4F13: Probabilistic Machine Learning, 2024-25
Module Leader
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:
|
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:
|
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:
|
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, 2022-23
Module Leader
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:
|
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:
|
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:
|
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
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:
|
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:
|
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:
|
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
Engineering Tripos Part IIB, 4F13: Probabilistic Machine Learning, 2018-19
Module Leader
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 course website: http://mlg.eng.cam.ac.uk/teaching/4f13/
Coursework
| Coursework | Format |
Due date & marks |
|---|---|---|
|
[Coursework activity #1 Gaussian Processes] Coursework 1 brief description Learning objective:
|
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:
|
Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs |
Fri week 7 [20/60] |
| [Coursework activity #3 Latent Dirichlet Allocation models or documents] Coursework 3 brief description Learning objective:
|
Individual Report Anonymously marked for MPHIL/MLSALT & Undergraduates Nonanonymously marked for PhDs |
Fri week 9 [20/60] |
Booklists
Please see the Booklist for Group F 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.
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: 17/05/2018 14:24
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