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
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, 2023-24
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: 30/05/2023 15:31
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, 2020-21
Module Leader
Lecturers
Prof Z Ghahramani & Dr M Hernandez-Lobato
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: 07/10/2020 16:34
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, 2019-20
Module Leader
Lecturers
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 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 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: 27/09/2019 12:18
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, 2025-26
Module Leader
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:
|
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: 04/06/2025 13:30
Engineering Tripos Part IIB, 4F12: Computer Vision, 2020-21
Module Leader
Lecturers
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 the limitations of current methods.
Content
- Introduction (1L)
Computer vision: what is it, why study it and how ? The eye and the camera, vision as an information processing task. A geometrical framework for vision. 3D interpretation of 2D images. Applications.
- Image structure (3L)
Image intensities and structure: edges, corners and blobs. Edge detection, the aperture problem. Corner and blob detection. Contour extraction using B-spline snakes. Texture. Feature descriptors and matching.
- Projection (3L)
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 (3L)
Epipolar geometry and the essential matrix. Recovery of depth. Uncalibrated cameras and the fundamental matrix. The correspondence problem. Structure from motion. 3D shape from multiple view stereo.
- Object detection and recognition (3L)
Basic architectures for deep learning in computer vision. Object detection, classification and semantic segmentation. Object recognition, feature embedding and metric learning. Reconstruction, localisation and structured deep learning.
- Example classes (3L)
Discussion of examples papers and past examination papers.
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.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
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.
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.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 17/09/2020 08:07
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