Undergraduate Teaching 2025-26

2025-26

2025-26

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Engineering Tripos Part IIB, 4M19: Advanced Building Physics, 2025-26

Module Leader

Prof G Hunt

Lecturers

Prof Gary Hunt, Prof R Choudhary, Prof S Fitzgerald

Timing and Structure

16 lectures (including integrated examples classes) + coursework; Assessment: 100% coursework

Aims

The aims of the course are to:

  • To develop a deep understanding of principles of building physics at the system level to guide the design of zero-carbon buildings
  • To understand methods and tools used for quantifying energy efficiency of buildings
  • To understand the design of heating, cooling, and ventilation in buildings

Objectives

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

  • evaluate alternative energy systems and buildings technologies against energy consumption for a given context.
  • design and evaluate energy efficiency of buildings
  • understand the factors that influence and control the movement of air and heat in naturally ventilated buildings.

Content

Designing sustainable buildings requires making choices among various building materials and components, and more efficient use of energy and natural resources. In order to do so, the building structure, the building fabric and the building services must be understood both in individual detail and as interacting systems. For example, the need for energy must be analysed in conjunction with energy production for heating and cooling, distribution, thermal storage and the end-use in buildings. The module first introduces students to energy-efficient building systems and other advanced building physics topics. It subsequently describes energy modelling techniques for analysing buildings as a system of interacting components and processes leading to low-energy buildings that satisfy occupant comfort systems and technologies. The module aims to develop a deep understanding of how fundamental principles of building physics are integrated at the system level to guide the design of zero-carbon built environments.

Energy Efficient Building Systems (2 hours, Fitzgerald)

  • Building physics in the context of climate change
  • Integrated design of heating, cooling, and ventilation systems

Ventilation: creating air movements for the supply of fresh air and removal of stale air (10 hours, Hunt)

  • Natural ventilation of modern buildings
  • Displacement ventilation & thermally stratified flows
  • Mixing ventilation
  • Airflow through vents
  • Transient flows through rooms & night purging
  • Steady flows through rooms & heat source modelling
  • Sizing ventilation openings
  • Low-energy design

Building Performance Modelling (4 hours, Choudhary)

  • Introduction to data-driven performance assessment 
  • Integrated design of heating, cooling, and ventilation systems

Further notes


Examples papers


Coursework

Assignment 1: natural ventilation strategy of a classroom. (15%)

Assignment 2: consists of using sensors to monitor a space in the department and analyse its performance. (15%)

Assignment 3: drawing directly from the ventilation lectures, consists of an in-class exercise to map out (qualitatively and quantitatively) the preliminary design of a low-energy, naturally ventilated building. (70%)

 

 

 

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.

 
Last modified: 09/10/2025 16:05

Engineering Tripos Part IIB, 4M17: Practical Optimisation, 2025-26

Module Leader

Prof G Wells

Lecturers

Dr Joe Dean, Dr T Kipouros

Timing and Structure

Michaelmas Term. 13 lectures + 3 coursework sessions. Assessment: 100% coursework. Lectures will be recorded.

Prerequisites

3M1

Aims

The aims of the course are to:

  • Teach some of the basic optimisation methods used to tackle difficult, real-world optimisation problems.
  • Teach means of assessing the tractability of nonlinear optimisation problems.
  • Develop an appreciation of practical issues associated with the implementation of optimisation methods.
  • Provide experience in applying such methods on challenging problems and in assessing and comparing the performance of different algorithms.

Objectives

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

  • Understand the basic mathematics underlying linear and convex optimisation.
  • Be able to write and benchmark simple algorithms to solve a convex optimisation problem.
  • Understand the technique of Markov-Chain Monte Carlo simulation, and apply it to solve a Travelling Salesman Problem.
  • Understand the ways in which different heuristic and stochastic optimisation methods work and the circumstances in which they are likely to perform well or badly.
  • Understand the principles of multiobjective optimization and the benefits of approaching real-world optimisation problems from a multiobjective perspective.

Content

  • Introduction (what is Practical Optimisation?)
  • Approximately solving Ax=b (various methods of norm minimization of residuals that lead to LP or convex problems)
  • Geometry of polyhedral and convex sets (review of the simplex method; introduction to algorithmic complexity)
  • Duality theory and its applications
  • Unconstrained optimisation
  • Important convex relaxations in cardinality problems 
  • Circumstances in which 'methods of last resort' are needed
  • Simulated Annealing: basic concepts, solution representation and generation, the annealing schedule, enhancements and modifications
  • Genetic Algorithms: basic concepts, solution representation, selection, crossover, mutation
  • Tabu Search: basic concepts, solution representation, local search, intensification, diversification
  • Multiobjective Optimization: archiving, multiobjective simulated annealing, multiobjective genetic algorithms
  • Case Study: multiobjective optimization of pressurised water reactor reload cores

Coursework

Coursework

Format

Due date

& marks

Coursework activity #1: Training a support vector machine for data classification

Learning objective:
  • Create an Interior Point Method implementation for solving convex optimisation problems.
  • Use an Interior Point Method to train and explore a support vector machine for data classification.

Individual report

anonymously marked

Deadline: After end of Michaelmas Term (see VLE for exact deadline)

[30/60]

Coursework activity #2: Investigation of the performance of two stochastic optimization methods on a hard problem

Learning objective:

  • Gain experience in applying stochastic optimisation methods to challenging problems
  • Explore and analyse the variation in optimiser performance as algorithm control parameters are modified
  • Compare and analyse the performance of different optimisation methods on challenging problems

Individual report

anonymously marked

Deadline: Before start of Lent Term (see VLE for exact deadline)

[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.

Intellectual Abilities

Knowledge and Understanding

Practical skills

Engineering Analysis (E)

Underpinning Science and Mathematics and associated engineering disciplines

 
Last modified: 19/10/2025 17:17

Engineering Tripos Part IIB, 4M16: Nuclear Power Engineering (shared with IIA), 2025-26

Module Leader

Dr Paul Cosgrove

Lecturers

Dr Paul Cosgrove and Mr Bob Skelton

Timing and Structure

Lent Term. 12 lectures + 2 examples classes + 2 in-lecture demonstrations. Assessment: 100% exam. Lectures will be recorded.

Aims

The aims of the course are to:

  • give the student an introduction to and appreciation of nuclear power engineering and the UK nuclear industry

Objectives

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

  • appreciate the nature of neutron-nucleus interactions
  • classify ionising radiation by physical nature and health hazard
  • conduct safely a simple experiment involving radiation
  • understand the principles of radiation detection and shielding
  • understand the principles of operation of UK nuclear reactors
  • apply elementary models of neutron behaviour in reactors
  • compute simple power distributions in reactors
  • compute simple temperature distributions in reactors and appreciate their consequences
  • appreciate the significance of delayed neutrons and xenon-135 to the control and operation of reactors
  • appreciate the advantages and disadvantages of on-load and off-load refuelling
  • perform simple calculations to predict the refuelling requirements of reactors
  • explain the operation of enrichment plant
  • appreciate the problems of radioactive waste management
  • appreciate the range of activities of the UK nuclear industry

Content

This module aims to give the student an introduction to and appreciation of nuclear power engineering and the UK nuclear industry, particularly the technology used in the production of electricity in nuclear power stations, the preparation and subsequent treatment of the fuel and its by-products, and the detection of ionising radiation and the protection of workers within the nuclear industry and the general public from it.

Basic Principles and Health Physics (2L, Dr P.M. Cosgrove)

  • Principles of nuclear reactions;
  • Radioactivity and the effects of ionising radiation;
  • Introduction to health physics and shielding.

Reactor Physics (3L, Dr P.M. Cosgrove)

  • The fission chain process;
  • Interactions of neutrons with matter;
  • Models for neutron distributions in space and energy.

Reactor Design & Operation (4L, Dr P.M. Cosgrove)

  • Simple reactor design;
  • Heat transfer and temperature distributions in commercial reactors;
  • Time-dependent aspects of reactor operations; delayed neutrons and xenon poisoning;
  • In-core and out-of-core fuel cycles.

Fuel Processing (3L, Mr R.L. Skelton)

  • Enrichment and reprocessing;
  • The treatment, containment and disposal of radioactive wastes.

Demonstrations (2L, Dr P.M.Cosgrove)

Demonstration of the use of Geiger-Muller and scintillation counters for detecting ionising radiation (1 hour in-lecture time).

Demonstration of the detection and shielding of fast and thermal neutrons using a 37 GBq Americium-Beryllium source (1 hour in-lecture time).

Booklists

Please refer to the Booklist for references to this module. This can be found on the associated Moodle course.

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 04/06/2025 13:33

Engineering Tripos Part IIB, 4M12: Partial Differential Equations & Variational Methods (shared with IIA), 2025-26

Module Leader

Prof J Biggins

Lecturers

Dr J Li and Prof J Biggins

Timing and Structure

Lent term. 16 lectures (including examples classes). Assessment: 100% exam

Aims

The aims of the course are to:

  • provide an introduction to the various classes of PDE and the physical nature of their solution
  • demonstrate how variational calculus can be used to derive both ordinary and partial differential equations, and also how the technique can be used to obtain approximate solutions to these equations

Objectives

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

  • understand the various types of PDE and the physical nature of their solutions.
  • understand various solution methods for PDEs and be able to apply these to a range of problems.
  • understand the formulation of various physical problems in terms of variational statements
  • estimate solutions using trial functions and direct minimisation;
  • calculate an Euler-Lagrange differential equation from a variational statement, and to find the corresponding natural boundary conditions;
  • perform vector manipulations using suffix notation.

Content

Partial differential equations (PDEs) occur widely in all branches of engineering science, and this course provides an introduction to the various classes of PDE and the physical nature of their solution. The second part of the course demonstrates how variational calculus can be used to derive both ordinary and partial differential equations, and also how the technique can be used to obtain approximate solutions to these equations. The final section on the summation convention provides a powerful mathematical tool for the manipulation of equations that arise in engineering analysis

Suffix notation and the summation convention (2L Prof J S Biggins)

Index notation for scalar, vector, and matrix products, and for grad, div and curl. Applications including Stokes’ theorem and the divergence theorem.

Variational methods in engineering analysis (6L Prof J S Biggins)

Introduction to variational calculus. Functionals and their first variation. Derivation of differential equations and boundary conditions from variational principles. The Euler-Lagrange equations. The effect of constraints. Applications in mechanics, optics, stress analysis, and optimal control.

Partial Differential Equations (8L Dr J Li)

What is a PDE? Classification of PDEs: elliptic/parabolic/hyperbolic types. Canonical examples of each type: Laplace/diffusion/wave equations. Typical solution techniques and example solutions for simple geometries.

Booklists

Please refer to the Booklist 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.

E3

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

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.

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 13:33

Engineering Tripos Part IIB, 4M2: German, 2025-26

Module Leader

Jan-Moritz Bogdanovic

Secondary point of contact for queries

Prof. David Tual

Timing and Structure

Lent term. Assessment: 100% coursework

Prerequisites

German at Upper Intermediate Level or higher. In any case, students wishing to take a language module must contact the relevant language coordinator in order to ensure they hold the necessary qualifications.

Aims

The aims of the course are to:

  • improve understanding of German technology, society and culture;
  • enable all students to consolidate their listening skills and practise their speaking skills in class, while particular emphasis will be put on reading and writing skills outside the class;
  • improve understanding of how AI can be used for writing skills development.

Objectives

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

  • Be confident in communicating in the target language, especially in a work-related situation, as well as explaining and defending their opinion about specific issues and problems
  • Use the language as a tool to improve their understanding of technology and culture
  • Analyse a topic/an issue presented in German language, compare all the elements at play, synthesise the major points and make a balanced judgement
  • Reflect critically on the appropriate and effective use of AI.

Content

This module will significantly enhance students’ receptive language skills so that, at the end of this course, students will be able to follow lectures and presentations in their subject area held in German as well as participate actively in question-and-answer sessions on engineering-related topics. By regular training and application of specific productive/expressive language skills, they will further improve their ability to take part in discussions of both general and engineering-related issues. Students will develop the ability to use and critically reflect on AI for writing skills development.

7 Lectures (various speakers) + 7 seminars (module leader)

  • Presentations on engineering/science in German (5-6 Lectures)
  • Presentations on cultural/social topics in German (1-2 Lectures)

Seminars

Associated with each lecture will be a one-hour seminar. This may be held before the lecture for preparation, or following the lecture for discussion purposes.

Format may vary.

 

Further notes

A list of this year's module talks will be available on Moodle.

Coursework

The students will prepare 3 major pieces of coursework:

  • Two written reports (25% each)
  • Oral presentation (50%)

The assignments will be marked for language and/or content. In the case of native–speakers, the quality of the language production will be assessed accordingly.

 

Coursework

Format

Due date

& marks

Coursework activity #1 Report

A structured report of 700 words in the target language. Students should not use any online writing aid other than dictionaries. They should attach a list of the words they looked up as well as any reference material used (e.g. grammar books or websites). This assignment will be assessed for content and report structure, not language (although language mistakes will be flagged up as part of formative feedback, providing the students with the opportunity to reflect and self-correct).

Learning objective: 

·       Analyse, synthesise and/or critically evaluate a topic presented and discussed in class (topic related to science, technology or the culture of the German-speaking world);
 

·       Express ideas in a logical and articulate manner using a range of structures and expressions appropriate to the task and expected at the level of proficiency in the target language.

Individual report (700 words)

Non-anonymously marked

End of week 3

[25%]

Coursework activity #2 Report

A structured report of 500 words in the target language AND a revised draft (500 words). Student should submit a first draft produced without any aid at all, as well as a revised draft produced with the help AI (e.g. ChatGPT). They should be able to explain and justify the changes they chose to make and included in the revised draft (as this could be explored during the oral presentation). This assignment will be assessed for content and language (including the ability to reflect, self-correct and use AI appropriately).

Learning objective: 

·       Analyse, synthesise and/or critically evaluate a topic presented and discussed in class (topic related science, technology or the culture of the German-speaking world)
 

·       Express ideas in a logical and articulate manner using a range of structures and expressions appropriate to the task and expected at the level of proficiency in the target language.

 

·       Use AI appropriately and critically.

Individual report (1000 words)

Non-anonymously marked

End of week 5

[25%]

Coursework activity #3 Oral presentation

A structured oral presentation (5 minutes), followed by questions on content and/or language about the presentation and/or the two written assignments (10-12 minutes). This assignment will be assessed for content and language (including the ability to reflect, self-correct and use AI appropriately).

Learning objective: 

·       Analyse, synthesise and/or critically evaluate a topic presented and discussed in class (a topic related to science, technology or the culture of the German-speaking world)
 

·       Express ideas in a logical and articulate manner using a range of structures and expressions appropriate to the task and expected at the level of proficiency in the target language

 

·       Demonstrate an understanding of the target language and the ability to reflect critically on their language learning experience and the use of AI.

Individual oral presentation (5 minutes) followed by questions (10-12 minutes)

Non-anonymously marked

Last session (week 8)

[50%]

 

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.

P4

Understanding use of technical literature and other information sources.

 
Last modified: 04/06/2025 13:33

Engineering Tripos Part IIB, 4M1: French, 2025-26

Module Leader

Prof. David Tual

Lecturer

Prof. David Tual

Timing and Structure

Lent term. 7 lectures + seminars + coursework. Assessment: 100% coursework.

Prerequisites

Modules can be chosen by students with at least a B1/B2 (CEFR) level in the respective language (i.e. equivalent to AS or A-level). In any case, students wishing to take a language module must contact the relevant language coordinator in order to ensure they hold the necessary qualifications.

Aims

The aims of the course are to:

  • improve understanding of French technology, society and culture;
  • enable all students to consolidate their listening skills and practise their speaking skills in class, while particular emphasis will be put on reading and writing skills outside the class;
  • improve understanding of how AI can be used for writing skills development.

Objectives

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

  • be confident in speaking/reading/writing whether in a general or work-related situation;
  • use the language as a tool to improve understanding of technology, society and culture;
  • analyse a topic/an issue in depth, compare all the elements at play, synthesise the major points and make a balanced judgement;
  • reflect critically on the appropriate and effective use of AI.

Content

Seminars (7 Lectures, various speakers, subject to changes)

  • L'industrie des matériaux composites
  • La politique française
  • La cristallographie quantique 
  • Ingénieurs Sans Frontières
  • Mai 68
  • La nanostructuration spontanée
  • Présentation du CEA

Seminars

Associated with each lecture will be a one-hour seminar. This may be held before the lecture for preparation, or following the lecture for discussion purposes.

Format may vary.

Coursework

The students will prepare 3 major pieces of coursework:

  • Two written reports (25% each)
  • Oral presentation (50%)

The assignments will be marked for language and/or content. In the case of native–speakers, the quality of the language production will be assessed accordingly.

 

Coursework

Format

Due date

& marks

Coursework activity #1 Report

A structured report of 900 words in the target language. Students should not use any online writing aid other than dictionaries. They should attach a list of the words they looked up as well as any reference material used (e.g. grammar books or websites). This assignment will be assessed for content and report structure, not language (although language mistakes will be flagged up as part of formative feedback, providing the students with the opportunity to reflect and self-correct).

Learning objective: 

·       Analyse, synthesise and/or critically evaluate a topic presented and discussed in class (topic related to science, technology or the culture of the French-speaking world);
 

·       Express ideas in a logical and articulate manner using a range of structures and expressions appropriate to the task and expected at the level of proficiency in the target language.

Individual report (900 words)

Non-anonymously marked

End of week 3

[25%]

Coursework activity #2 Report

A structured report of 600 words in the target language AND a revised draft (600 words). Student should submit a first draft produced without any aid at all, as well as a revised draft produced with the help AI (e.g. ChatGPT). They should be able to explain and justify the changes they chose to make and included in the revised draft (as this could be explored during the oral presentation). This assignment will be assessed for content and language (including the ability to reflect, self-correct and use AI appropriately).

Learning objective: 

·       Analyse, synthesise and/or critically evaluate a topic presented and discussed in class (topic related science, technology or the culture of the French-speaking world)
 

·       Express ideas in a logical and articulate manner using a range of structures and expressions appropriate to the task and expected at the level of proficiency in the target language.

 

·       Use AI appropriately and critically.

Individual report (1200 words)

Non-anonymously marked

End of week 5

[25%]

Coursework activity #3 Oral presentation

A structured oral presentation (5 minutes), followed by questions on content and/or language about the presentation and/or the two written assignments (10-12 minutes). This assignment will be assessed for content and language (including the ability to reflect, self-correct and use AI appropriately).

Learning objective: 

·       Analyse, synthesise and/or critically evaluate a topic presented and discussed in class (a topic related to science, technology or the culture of the French-speaking world)
 

·       Express ideas in a logical and articulate manner using a range of structures and expressions appropriate to the task and expected at the level of proficiency in the target language

 

·       Demonstrate an understanding of the target language and the ability to reflect critically on their language learning experience and the use of AI.

Individual oral presentation (5 minutes) followed by questions (10-12 minutes)

Non-anonymously marked

Last session (week 8)

[50%]

 

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.

P4

Understanding use of technical literature and other information sources.

 
Last modified: 04/06/2025 13:33

Engineering Tripos Part IIB, 4G3: Computational Neuroscience, 2025-26

Module Leader

Prof Máté Lengyel

Lecturers

Prof G Hennequin, Dr Y Ahmadian and Prof M Lengyel

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:

  • develop an understanding of the fundamentals of reinforcement learning, and how they relate to neural and behavioural data on the ways in which the brain learns from rewards
  • demonstrate the importance of internal models in neural computations, and provide examples for their behavioural and neural signatures
  • introduce alternative ways of modelling single neurons, and the way these single neuron models can be integrated into models of neural networks.
  • explain how the dynamical interactions between neurons give rise to emergent phenomena at the level of neural circuits
  • 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
  • demonstrate case studies of computational functions that neural networks can implement

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 (9L, M Lengyel)

  • introduction: the goals of computational neuroscience, levels of analysis, and module plan
  • reinforcement learning: theoretical background and basic theorems, alternative algorithmic solutions and multiple leaerning & memory systems, model-based vs. model-free computations, the temporal difference learning theory of dopamine responses
  • internal models: theoretical framework, internal models in perception, sensori-motor control, statistical learning, structure learning, neural correlates, neural representations of unceratinty, representational learningr
  • associative memory: the Hebbian paradigm, attractor neural networks, the Hopfield network, energy function, capacity, place cells, place cells, long-term plasticity, and navigation, place cell remapping

Network dynamics & Plasticity (4L, G Hennequin)

  • linear and non-linear network dynamics
  • spiking neural network dynamics
  • excitatory-inhiitory balance
  • chaotic dynamics
  • network mechanisms of selective amplification
  • orientation tuning in primary visual cortex

Plasticity & Biophysics (3L, Y Ahmadian)

  • Hebbian plasticity
  • spike timing-dependant plasticity
  • learning receptive fields
  • biohysical models of single neurons
  • biohysical models of simple circuits

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: network dynamics

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

Individual report

Anonymously marked

Posted week 4

Due week 6

[30/60]

Coursework activity #2: synaptic plasticity and representational learning

The brain constantly reconfigures itself via synaptic plasticity to develop useful representations of its inputs. In this courtsework you will build and analyse simple models to understand some of the basic principles underlying this process

Learning objective:

  • implement symple models of synaptic plasticity and analyse how they lead to pattern formation in feedforward and recurrent networks
  • implement a divisive normalisation model of visual cortical responses and analyse how it achieves efficient coding of natural image inputs and explains non-classical receptive field efffects in the reponses of simple cells in the primary visual cortex

Individual Report

Anonymously marked

Posted week 8

Due two weeks later

[30/60]

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

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.

 
Last modified: 04/06/2025 13:30

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]

 

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, 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]

 

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, 4F12: Computer Vision, 2025-26

Module Leader

Prof R Cipolla

Lecturers

Prof R Cipolla and Dr Matthew Johnson

Timing and Structure

Michaelmas term. 16 lectures (including 3 examples classes). Assessment: 100% exam

Aims

The aims of the course are to:

  • introduce the principles, models and applications of computer vision.
  • cover image structure, projection, stereo vision, structure from motion and object detection and recognition.
  • give case studies of industrial (robotic) applications of computer vision, including visual navigation for autonomous robots, robot hand-eye coordination and novel man-machine interfaces.

Objectives

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

  • design feature detectors to detect, localise and track image features.
  • model perspective image formation and calibrate single and multiple camera systems.
  • recover 3D position and shape information from arbitrary viewpoints;
  • appreciate the problems in finding corresponding features in different viewpoints.
  • analyse visual motion to recover scene structure and viewer motion, and understand how this information can be used in navigation;
  • understand how simple object recognition systems can be designed so that they are independent of lighting and camera viewpoint.
  • appreciate the commerical and industrial potential of computer vision but understand its limitations.

Content

  • Introduction (1L)
    Computer vision: what is it, why study it and how ? The eye and the camera, vision as an information processing task. 3D interpretation of 2D images. Geometrical, statistical and learning frameworks for vision. Applications.
     
  • Image structure (4L)
    Image intensities and structure: edges, corners and blobs. Edge detection, the aperture problem and corner detection. Image pyramids, blob detection with band-pass filtering. The SIFT feature descriptor for matching. Characterising textures.
     
  • Projection (4L)
    Orthographic projection. Planar perspective projection. Vanishing points and lines. Projection matrix, homogeneous coordinates. Camera calibration, recovery of world position. Weak perspective and the affine camera. Projective invariants. 
     
  • Stereo vision and Structure from Motion (2L)
    Epipolar geometry and the essential matrix. Recovery of depth by triangulation. Uncalibrated cameras and the fundamental matrix. The correspondence problem. Structure from motion. 3D shape examples from multiple view stereo.
     
  • Deep Learning for Computer Vision  (5L)
    Basic architectures for deep learning in computer vision. Object detection, classification and semantic segmentation. Object recognition, feature embedding and metric learning. Transformer architectures and self-supervised learning. 
     
  • Example classes
    Discussion of examples papers and past examination papers will be integrated with lectures.

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.

 
Last modified: 04/06/2025 13:30

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