Undergraduate Teaching 2025-26

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Engineering Tripos Part IIB, 4M24: Computational Statistics and Machine Learning, 2022-23

Leader

Prof M Girolami

Timing and Structure

Michaelmas term. 75% exam / 25% coursework. Lectures will be recorded.

Prerequisites

3F3, 3F8, 3M1

Aims

The aims of the course are to:

  • Introduce students to foundational theoretical concepts and methodological tools essential for the successful development, analysis, and application of advanced Machine Learning and Computational Statistical methods.

Objectives

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

  • Introduce the students to the required statistical and mathematical concepts that underpin all rigorously designed Machine Learning and Computational Statistical methods that can be used practically across all the contemporary engineering sciences
  • Introduce the students to advanced computational statistical inference methods required to design Machine Learning solutions to a range of challenging large scale engineering problems where data and models are synthesised

Content

By successful completion of this course the student will have an appreciation and basic understanding of the mathematical, probabilistic, and statistical foundations of modern Computational Statistical Methods and recent developments in Machine Learning algorithms.

Computational Statistics and Machine Learning

Lecture.1. Monte Carlo Methods - A : Numerically computing integrals, the law of large numbers for Monte Carlo estimators, The Central Limit Theorem for Monte Carlo estimators.

Lecture.2. Monte Carlo Methods - B : Improving MC estimators, Importance Sampling, Control Variates to reduce variance of estimates.

Lecture.3. Lebesgue Integral and Measure - A : Difference between Riemann and Lebesgue Integral, why Lebesgue integral is required for machine learning and engineering, definition of Lebesgue integral.

Lecture.4. Lebesgue Integral and Measure - B : Definition of Lebesgue Measure, Radon-Nikodym derivative and change of measure, Measure theoretic basis of Probability (Kolmogorov), Random Variables.

Lecture.5. Markov Chain Monte Carlo - A : Definition of Markov chain and invariant distributions, presentation of the Metropolis and Hastings method.

Lecture.6. Markov Chain Monte Carlo - B : Metropolis Hastings in multiple dimensions, the Gibbs Sampler.

Lecture.7. Vector, Metric, and Banach Spaces : generalisation of Euclidean space in R^3 to infinite dimensional spaces, Completion of space and definition of Banach space of functions.

Lecture.8. Hilbert Spaces : Inner product space, definition of Hilbert space, Cauchy sequences and function approximation, Reproducing kernel Hilbert Space and function approximation.

Lecture.9. Sobolev Spaces : Definition of weak derivatives, understanding rates of convergence of function approximations based on properties of Sobolev space (smoothness)

Lecture.10. Gaussian Measure in Hilbert Space : Illustrating non-existence of Lebesgue Measure in function space, construction of finite Gaussian measure in Hilbert space, definition of Bayes rule (via Radon-Nikodym derivative) in Hilbert space employing Gaussian measure as reference - GP's.

Lecture.11. MCMC in Hilbert space : defining dimension invariant Markov transition kernel in Hilbert space and how overcomes degeneracy in high dimensions.

Lecture.12. Langevin Dynamics Simulation I - use of Langevin dynamics to simulate from a desired probability measure.

Lecture.13. Langevin Dynamics Simulation II  - use of approximate Langevin dynamics to simulate from a desired probability measure.

Lecture.14. Parallel Tempering - indtroduction to simulation from multi-modal target probability measures. 

Further notes

Machine Learning methods are having a major impact in every area of the engineering sciences. Machine Learning models and methods rely predominantly on Computational Statistics methods for model calibration, estimation, prediction and updating. Together Computational Statistics and Machine Learning are providing a revolution in the way mankind lives, works, communicates, and transacts.

Machine Learning methodology is not a magic wand that once waved will mysteriously solve long standing technical problems. There are underlying mathematical and statistical theories and principles which define these Machine Learning methods and it is important for the Machine Learning practitioner to have some understanding of them. This course is complementary to current Machine Learning modules in the Engineering Tripos.

This course will provide an overview and very basic introduction to a subset of the major theoretical and methodological ideas that underpin much of Machine Learning. It will provide the student with an appreciation of the possibilities and limitations of Machine Learning and Computational Statistics. This should be a launch pad for students wishing to gain a greater in-depth understanding of Machine Learning  as both practitioner and researcher.

Coursework

Coursework Format

Due date

& marks

Simulation Based Inference on Engineering Problem

The synthesis of both data and formal mathematical models in defining a digital twin of an engineering problem will be presented. The design of the machine learning and computational statistical methods to characterise uncertainty in predictions and forecasts from the digital twin will be the main focus of this exercise.

Learning objective:

  • To take an engineering problem and define appropriate mathematical, machine learning and data modelling strategies in studying the characteristics of the engineering system or artefact.
  • To successfully implement and deploy computational statistical methods in delivering an uncertainty quantification strategy in the specific engineering problem.

Individual Report

anonymously marked

  Wed week 9

[15/60]

 

Booklists

Shima, H. Functional Analysis for Physics and Engineering: An Introduction, CRC Press.

Biegler, L., Biros, G., Ghattas, O., Heinkenschloss, M., Keyes, D., Mallickj, B., Tenorio, L., van Bloemen Waanders, B., Willcox, K., and Marouk, Y. (2010). Large-Scale Inverse Problems and Quantification of Uncertainty. Wiley.

Brooks, S., Gelman, A., Jones, G. L., and Meng, X. (2011). Handbook of Markov Chain Monte Carlo. CRC.

Cotter, C. and Reich, S. (2015). Probabilistic Forecasting and Bayesian Data Assimilation. Cambridge University Press.

Law, K., Stuart, A., and Zygalakis, K. (2015). Data Assimilation: A Mathematical Introduction. Springer.

Rogers, S. and Girolami, M. (2016). A First Course in Machine Learning, 2nd Edition. CRC.

Sullivan, T. J. (2015). Introduction to Uncertainty Quantification. Springer.

 

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 19/07/2022 08:09

Engineering Tripos Part IIB, 4M24: Computational Statistics and Machine Learning, 2021-22

Leader

Prof M Girolami

Timing and Structure

Michaelmas term. 75% exam / 25% coursework.

Prerequisites

3F3, 3F8, 3M1

Aims

The aims of the course are to:

  • Introduce students to foundational theoretical concepts and methodological tools essential for the successful development, analysis, and application of advanced Machine Learning and Computational Statistical methods.

Objectives

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

  • Introduce the students to the required statistical and mathematical concepts that underpin all rigorously designed Machine Learning and Computational Statistical methods that can be used practically across all the contemporary engineering sciences
  • Introduce the students to advanced computational statistical inference methods required to design Machine Learning solutions to a range of challenging large scale engineering problems where data and models are synthesised

Content

By successful completion of this course the student will have an appreciation and basic understanding of the mathematical, probabilistic, and statistical foundations of modern Computational Statistical Methods and recent developments in Machine Learning algorithms.

Computational Statistics and Machine Learning

Lecture.1. Monte Carlo Methods - A : Numerically computing integrals, the law of large numbers for Monte Carlo estimators, The Central Limit Theorem for Monte Carlo estimators.

Lecture.2. Monte Carlo Methods - B : Improving MC estimators, Importance Sampling, Control Variates to reduce variance of estimates.

Lecture.3. Lebesgue Integral and Measure - A : Difference between Riemann and Lebesgue Integral, why Lebesgue integral is required for machine learning and engineering, definition of Lebesgue integral.

Lecture.4. Lebesgue Integral and Measure - B : Definition of Lebesgue Measure, Radon-Nikodym derivative and change of measure, Measure theoretic basis of Probability (Kolmogorov), Random Variables.

Lecture.5. Markov Chain Monte Carlo - A : Definition of Markov chain and invariant distributions, presentation of the Metropolis and Hastings method.

Lecture.6. Markov Chain Monte Carlo - B : Metropolis Hastings in multiple dimensions, the Gibbs Sampler.

Lecture.7. Vector, Metric, and Banach Spaces : generalisation of Euclidean space in R^3 to infinite dimensional spaces, Completion of space and definition of Banach space of functions.

Lecture.8. Hilbert Spaces : Inner product space, definition of Hilbert space, Cauchy sequences and function approximation, Reproducing kernel Hilbert Space and function approximation.

Lecture.9. Sobolev Spaces : Definition of weak derivatives, understanding rates of convergence of function approximations based on properties of Sobolev space (smoothness)

Lecture.10. Gaussian Measure in Hilbert Space : Illustrating non-existence of Lebesgue Measure in function space, construction of finite Gaussian measure in Hilbert space, definition of Bayes rule (via Radon-Nikodym derivative) in Hilbert space employing Gaussian measure as reference - GP's.

Lecture.11. MCMC in Hilbert space : defining dimension invariant Markov transition kernel in Hilbert space and how overcomes degeneracy in high dimensions.

Lecture.12. Control Functionals - use of RKHS to obtain super-root-N convergence of Monte Carlo Estimators.

Lecture.13. Hierarchic Bayesian models and MCMC for them using pseudo-marginal MCMC methodology.

Lecture.14. Russian Roulette simulation and inference for doubly intractable probability measures

Further notes

Machine Learning methods are having a major impact in every area of the engineering sciences. Machine Learning models and methods rely predominantly on Computational Statistics methods for model calibration, estimation, prediction and updating. Together Computational Statistics and Machine Learning are providing a revolution in the way mankind lives, works, communicates, and transacts.

Machine Learning methodology is not a magic wand that once waved will mysteriously solve long standing technical problems. There are underlying mathematical and statistical theories and principles which define these Machine Learning methods and it is important for the Machine Learning practitioner to have some understanding of them. This course is complementary to current Machine Learning modules in the Engineering Tripos.

This course will provide an overview and very basic introduction to a subset of the major theoretical and methodological ideas that underpin much of Machine Learning. It will provide the student with an appreciation of the possibilities and limitations of Machine Learning and Computational Statistics. This should be a launch pad for students wishing to gain a greater in-depth understanding of Machine Learning  as both practitioner and researcher.

Coursework

Coursework Format

Due date

& marks

Simulation Based Inference on Engineering Problem

The synthesis of both data and formal mathematical models in defining a digital twin of an engineering problem will be presented. The design of the machine learning and computational statistical methods to characterise uncertainty in predictions and forecasts from the digital twin will be the main focus of this exercise.

Learning objective:

  • To take an engineering problem and define appropriate mathematical, machine learning and data modelling strategies in studying the characteristics of the engineering system or artefact.
  • To successfully implement and deploy computational statistical methods in delivering an uncertainty quantification strategy in the specific engineering problem.

Individual Report

anonymously marked

  Wed week 9

[15/60]

 

Booklists

Shima, H. Functional Analysis for Physics and Engineering: An Introduction, CRC Press.

Biegler, L., Biros, G., Ghattas, O., Heinkenschloss, M., Keyes, D., Mallickj, B., Tenorio, L., van Bloemen Waanders, B., Willcox, K., and Marouk, Y. (2010). Large-Scale Inverse Problems and Quantification of Uncertainty. Wiley.

Brooks, S., Gelman, A., Jones, G. L., and Meng, X. (2011). Handbook of Markov Chain Monte Carlo. CRC.

Cotter, C. and Reich, S. (2015). Probabilistic Forecasting and Bayesian Data Assimilation. Cambridge University Press.

Law, K., Stuart, A., and Zygalakis, K. (2015). Data Assimilation: A Mathematical Introduction. Springer.

Rogers, S. and Girolami, M. (2016). A First Course in Machine Learning, 2nd Edition. CRC.

Sullivan, T. J. (2015). Introduction to Uncertainty Quantification. Springer.

 

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 31/05/2021 10:21

Engineering Tripos Part IIB, 4M24: Computational Statistics and Machine Learning, 2020-21

Leader

Prof M Girolami

Timing and Structure

Michaelmas term. 75% exam / 25% coursework.

Prerequisites

3F3, 3F8, 3M1

Aims

The aims of the course are to:

  • Introduce students to foundational theoretical concepts and methodological tools essential for the successful development, analysis, and application of advanced Machine Learning and Computational Statistical methods.

Objectives

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

  • Introduce the students to the required statistical and mathematical concepts that underpin all rigorously designed Machine Learning and Computational Statistical methods that can be used practically across all the contemporary engineering sciences
  • Introduce the students to advanced computational statistical inference methods required to design Machine Learning solutions to a range of challenging large scale engineering problems where data and models are synthesised

Content

By successful completion of this course the student will have an appreciation and basic understanding of the mathematical, probabilistic, and statistical foundations of modern Computational Statistical Methods and recent developments in Machine Learning algorithms.

Computational Statistics and Machine Learning

Lecture.1. Monte Carlo Methods - A : Numerically computing integrals, the law of large numbers for Monte Carlo estimators, The Central Limit Theorem for Monte Carlo estimators.

Lecture.2. Monte Carlo Methods - B : Improving MC estimators, Importance Sampling, Control Variates to reduce variance of estimates.

Lecture.3. Lebesgue Integral and Measure - A : Difference between Riemann and Lebesgue Integral, why Lebesgue integral is required for machine learning and engineering, definition of Lebesgue integral.

Lecture.4. Lebesgue Integral and Measure - B : Definition of Lebesgue Measure, Radon-Nikodym derivative and change of measure, Measure theoretic basis of Probability (Kolmogorov), Random Variables.

Lecture.5. Markov Chain Monte Carlo - A : Definition of Markov chain and invariant distributions, presentation of the Metropolis and Hastings method.

Lecture.6. Markov Chain Monte Carlo - B : Metropolis Hastings in multiple dimensions, the Gibbs Sampler.

Lecture.7. Vector, Metric, and Banach Spaces : generalisation of Euclidean space in R^3 to infinite dimensional spaces, Completion of space and definition of Banach space of functions.

Lecture.8. Hilbert Spaces : Inner product space, definition of Hilbert space, Cauchy sequences and function approximation, Reproducing kernel Hilbert Space and function approximation.

Lecture.9. Sobolev Spaces : Definition of weak derivatives, understanding rates of convergence of function approximations based on properties of Sobolev space (smoothness)

Lecture.10. Gaussian Measure in Hilbert Space : Illustrating non-existence of Lebesgue Measure in function space, construction of finite Gaussian measure in Hilbert space, definition of Bayes rule (via Radon-Nikodym derivative) in Hilbert space employing Gaussian measure as reference - GP's.

Lecture.11. MCMC in Hilbert space : defining dimension invariant Markov transition kernel in Hilbert space and how overcomes degeneracy in high dimensions.

Lecture.12. Control Functionals - use of RKHS to obtain super-root-N convergence of Monte Carlo Estimators.

Lecture.13. Hierarchic Bayesian models and MCMC for them using pseudo-marginal MCMC methodology.

Lecture.14. Russian Roulette simulation and inference for doubly intractable probability measures

Further notes

Machine Learning methods are having a major impact in every area of the engineering sciences. Machine Learning models and methods rely predominantly on Computational Statistics methods for model calibration, estimation, prediction and updating. Together Computational Statistics and Machine Learning are providing a revolution in the way mankind lives, works, communicates, and transacts.

Machine Learning methodology is not a magic wand that once waved will mysteriously solve long standing technical problems. There are underlying mathematical and statistical theories and principles which define these Machine Learning methods and it is important for the Machine Learning practitioner to have some understanding of them. This course is complementary to current Machine Learning modules in the Engineering Tripos.

This course will provide an overview and very basic introduction to a subset of the major theoretical and methodological ideas that underpin much of Machine Learning. It will provide the student with an appreciation of the possibilities and limitations of Machine Learning and Computational Statistics. This should be a launch pad for students wishing to gain a greater in-depth understanding of Machine Learning  as both practitioner and researcher.

Coursework

Coursework Format

Due date

& marks

Simulation Based Inference on Engineering Problem

The synthesis of both data and formal mathematical models in defining a digital twin of an engineering problem will be presented. The design of the machine learning and computational statistical methods to characterise uncertainty in predictions and forecasts from the digital twin will be the main focus of this exercise.

Learning objective:

  • To take an engineering problem and define appropriate mathematical, machine learning and data modelling strategies in studying the characteristics of the engineering system or artefact.
  • To successfully implement and deploy computational statistical methods in delivering an uncertainty quantification strategy in the specific engineering problem.

Individual Report

anonymously marked

  Wed week 9

[15/60]

 

Booklists

Shima, H. Functional Analysis for Physics and Engineering: An Introduction, CRC Press.

Biegler, L., Biros, G., Ghattas, O., Heinkenschloss, M., Keyes, D., Mallickj, B., Tenorio, L., van Bloemen Waanders, B., Willcox, K., and Marouk, Y. (2010). Large-Scale Inverse Problems and Quantification of Uncertainty. Wiley.

Brooks, S., Gelman, A., Jones, G. L., and Meng, X. (2011). Handbook of Markov Chain Monte Carlo. CRC.

Cotter, C. and Reich, S. (2015). Probabilistic Forecasting and Bayesian Data Assimilation. Cambridge University Press.

Law, K., Stuart, A., and Zygalakis, K. (2015). Data Assimilation: A Mathematical Introduction. Springer.

Rogers, S. and Girolami, M. (2016). A First Course in Machine Learning, 2nd Edition. CRC.

Sullivan, T. J. (2015). Introduction to Uncertainty Quantification. Springer.

 

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 08/02/2021 14:34

Engineering Tripos Part IIB, 4I15: Mobile Robot Systems, 2020-21

Leader

Dr A Prorok

Lecturer

Dr A Prorok

Lecturer

Dr F Iida

Lecturer

Dr F Forni

Timing and Structure

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

Prerequisites

4M20 useful; 3F2 useful; 3F3 useful

Aims

The aims of the course are to:

  • This course teaches the foundations of autonomous mobile robots, covering topics such as perception, motion control, and planning.
  • It also teaches algorithmic strategies that enable the coordination of multi-robot systems and robot swarms.
  • The course will feature several practical sessions with hands-on robot programming. The students will undertake mini-projects, which will be formally evaluated through a report and presentation.

Objectives

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

  • understand how to control a mobile robot;
  • understand how a robot perceives its environment;
  • understand how a robot plans actions (navigation paths);
  • know paradigms of coordination in systems of multiple robots;
  • know classical multi-robot problems and their solution methods;
  • Know how to use ROS (Robot Operating System, http://www.ros.org).

Content

  • Robot motion and control. Kinematics, control models, trajectory tracking.
  • Control architectures. Sensor-actuator loops, reactive path planning.
  • Sensing. Sensors, perception.
  • Localization. Markov localization, environment modeling, SLAM.
  • Navigation. Planning, receding horizon control.
  • Multi-robot systems I. Centralization vs. decentralization, robot swarms.
  • Multi-robot systems II. Consensus algorithms, graph-theoretic methods.
  • Multi-robot systems III. Task assignment.
  • Multi-robot systems IV. Multi-robot path planning.

Further notes

Requirements:

Students are expected to have laptops running Linux, with installations of ROS Kinetic and Gazebo. An installation guide will be provided.

Coursework

Students will be expected to hand in two reports and attend an individual questioning session.

Coursework

 Format

Due date

& marks

[Coursework activity #1 : Assignments]

Learning objectives:

The assignments will consist of two elements: (1) experimental work using a robot simulator and real robots, and (2) theory / understanding. The exercises will require data collection and analysis. The balance between practice and theory will depend on the exercise topic. Each student will submit a written report. 

Each assignment will be marked on a scale of 0-100, and will compose 30% of the mark.

Individual Report

anonymously marked

February 2021

60% (30% each) for assignment

[Coursework activity #2 : ]

Learning objectives:

A set of proposals will be announced at the start of term. Students will form groups of 2-3 and select a project proposal. Each proposal will include a core and a set of extensions; Engineering students will be expected to complete the extensions. 

The project will compose 40% of the mark and will be evaluated on a scale of 0-100. It will be handed in as group-work in groups of 2-3, and the report will clearly state what each group member contributed. The overall project mark will be composed by a report score (60%) and a presentation score (40%). Project marks will reflect the contribution of each team member. Every team member is expected to make a similar, significant contribution to the project, and where this happens all team members will receive the same mark. The report requirements will differ for students. Engineering students will hand in 6-page double-column report (conference-formatted)

 

Individual Report

anonymously marked

April 2021

40%

 

 

 

Booklists

Siegwart, R., Nourbakhsh, I.R. & Scaramuzza, D. (2004). Autonomous mobile robots. MIT Press. 

Thrun, S., Wolfram B. & Dieter F. (2005). Probabilistic robotics. MIT Press. 

Mondada, F. & Mordechai B. (2018) Elements of Robotics. Springer 

Siciliano, B. & Khatib, O. (2016) Springer handbook of robotics. Springer. 

Mesbahi, M. & Egerstedt, M. (2010) Graph theoretic methods in multiagent networks. Princeton University Press.

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 07/10/2020 08:53

Engineering Tripos Part IA, Computer-Aided Design, 2025-26

Lecturer

Dr Richard Roebuck

Timing and Structure

This course involves: a single lecture in week 1 of Michaelmas Term; a Tutorials sheet to work though; a Tasks sheet on which you will be assessed. Help desk support is available through the term. Marking occurs at (or before) three fixed sessions.

Aims

The aims of the course are to:

  • Gain a working knowledge of Computer-aided Design (CAD) solid modelling.
  • Learn how to translate ideas, designs and real world items into shapes, assemblies and animations within a solid modelling environment.

Objectives

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

  • Use our chosen professional CAD package to create models of engineering components and assemblies.
  • Representing ideas, designs and real world items in the CAD environment in a range of ways.
  • Create output from the CAD environment, including animations, so as to be able to communicate ideas in a range of ways.

Content

The IA Computer-aided Design (CAD) course runs in Michaelmas Term and focusses on learning, and being assessed on, the operation of a professional CAD package.

The delivery of this course (lecture, helpdesks and marking sessions) are detailed on the moodle page supporting this course.

 

Michaelmas Term

  • Introduction to Solidworks
  • Creating parts
  • Forming assemblies
  • Outputting drawings
  • "Revolving"
  • "Sweeping"
  • Shape creation involving repeated "patterns"
  • Surface creation
  • Forming sheet metal objects
  • Using the "toolbox" of standard parts
  • Using "design tables"
  • Animating objects
  • Analysing the motion of animated objects

 

Further notes

There is a moodle page supporting the course. 

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 05/06/2025 11:14

Engineering Tripos Part IA, Computer-Aided Design, 2024-25

Lecturer

Dr Richard Roebuck

Timing and Structure

This course involves: a single lecture in week 1 of Michaelmas Term; a Tutorials sheet to work though; a Tasks sheet on which you will be assessed. Help desk support is available through the term. Marking occurs at (or before) three fixed sessions.

Aims

The aims of the course are to:

  • Gain a working knowledge of Computer-aided Design (CAD) solid modelling.
  • Learn how to translate ideas, designs and real world items into shapes, assemblies and animations within a solid modelling environment.

Objectives

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

  • Use our chosen professional CAD package to create models of engineering components and assemblies.
  • Representing ideas, designs and real world items in the CAD environment in a range of ways.
  • Create output from the CAD environment, including animations, so as to be able to communicate ideas in a range of ways.

Content

The IA Computer-aided Design (CAD) course runs in Michaelmas Term and focusses on learning, and being assessed on, the operation of a professional CAD package.

The delivery of this course (lecture, helpdesks and marking sessions) are detailed on the moodle page supporting this course.

 

Michaelmas Term

  • Introduction to Solidworks
  • Creating parts
  • Forming assemblies
  • Outputting drawings
  • "Revolving"
  • "Sweeping"
  • Shape creation involving repeated "patterns"
  • Surface creation
  • Forming sheet metal objects
  • Using the "toolbox" of standard parts
  • Using "design tables"
  • Animating objects
  • Analysing the motion of animated objects

 

Further notes

There is a moodle page supporting the course. 

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 30/07/2024 08:45

Engineering Tripos Part IA, Engineering Drawing, 2024-25

Lecturer

Prof. Nathan Crilly

Timing and Structure

The course is introduced by a lecture (this will be recorded). Five self-paced workbooks must be completed. These are assessed in two scheduled mark up sessions. Helpdesk support is available prior to the scheduled mark up sessions.

Aims

The aims of the course are to:

  • demonstrate the role of engineering drawing in design and communication
  • develop skills in reading different types of engineering drawings
  • develop skills in producing different types of engineering drawings.

Objectives

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

  • read and produce orthographic projection drawings (with the correct arrangement of principal views)
  • distinguish first-angle drawings from third-angle drawings
  • read isometric drawings of simple and complex shapes
  • sketch simple shapes in isometric and combine them to generate more complex shapes
  • convert between isometric and orthographic drawings (drawing one based on the other)
  • read and produce auxiliary views (on orthographic projection drawings)
  • construct basic sequences of auxiliary views (projecting new views from the preceding views)
  • read and produce hidden detail on isometric and orthographic drawings
  • read and produce sectioning on isometric and orthographic drawings
  • construct isometric sketches from successive sections
  • read and produce isometric and orthographic drawings with basic dimensions
  • identify and correct over-dimensioning or under-dimensioning on drawings
  • read and produce drawings which account for the effects of simple dimensional variation.

Content

The course is divided into five topics, each delivered through a workbook. Each workbook provides explanations, examples and exercises, arranged into sub-topics.

1. Orthographic projection

1.1. The different kinds of drawing used on the course

1.2. Different types of lines and what they represent

1.3. How orthographic projections are constructed

1.4. The main two conventions for how orthographic projections are laid out 

1.5. The principal views which are often drawn in orthographic projections 

1.6. The reason that 2nd and 4th angle projections aren’t used (an appendix).

2. Isometric drawing

2.1. What isometric views are

2.2. How to sketch basic shapes

2.3. How to sketch circles, cylinders and spheres

2.4. How to represent locations, movements and forces

2.5. How to draw dimetric and trimetric views.

3. Auxiliary views

3.1. Identifying significant views of planes and lines

3.2. Projecting auxiliary views from principal views 

3.3. Methods for constructing auxiliary views

3.4. Projecting partial auxiliary views

3.5. Significant views of forces and moments

3.6. Considering isometric projections as auxiliary views (an appendix).

 

4. Sectioning

4.1. The presentation of hidden detail

4.2. The presentation of section views

4.3. Special rules for offset, partial, revolved, removed and successive sections 

4.4. Combining auxiliary views with section views to yield auxiliary sections 

4.5. Drawing sections in isometric views

4.6. Special rules for sectioning thin material (an appendix).

 

5. Dimensioning

5.1. Presenting measurements on drawings

5.2. Some principles of dimensioning

5.3. Problems with over-dimensioning and under-dimensioning 

5.4. Accounting for dimensional variation

 

Booklists

Please refer to the Booklist for Part IA 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.

General transferable skills

Intellectual Abilities

Knowledge and Understanding

Practical skills

Design (D)

Design is the creation and development of an economically viable product, process or system to meet a defined need. It involves significant technical and intellectual challenges and can be used to integrate all engineering understanding, knowledge and skills to the solution of real problems.

Engineering Practice (P)

Practical application of engineering skills, combining theory and experience, and use of other relevant knowledge and skills. This must include an appropriate combination of the majority of these outcomes.

 
Last modified: 02/08/2024 12:04

Engineering Tripos Part IA, Computer-Aided Design, 2023-24

Lecturer

Dr Richard Roebuck

Timing and Structure

This course involves: a single lecture in week 1 of Michaelmas Term; a Tutorials sheet to work though; a Tasks sheet on which you will be assessed. Help desk support is available through the term. Marking occurs at (or before) three fixed sessions.

Aims

The aims of the course are to:

  • Gain a working knowledge of Computer-aided Design (CAD) solid modelling.
  • Learn how to translate ideas, designs and real world items into shapes, assemblies and animations within a solid modelling environment.

Objectives

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

  • Use our chosen professional CAD package to create models of engineering components and assemblies.
  • Representing ideas, designs and real world items in the CAD environment in a range of ways.
  • Create output from the CAD environment, including animations, so as to be able to communicate ideas in a range of ways.

Content

The IA Computer-aided Design (CAD) course runs in Michaelmas Term and focusses on learning, and being assessed on, the operation of a professional CAD package.

The delivery of this course (lecture, helpdesks and marking sessions) are detailed on the moodle page supporting this course.

 

Michaelmas Term

  • Introduction to Solidworks
  • Creating parts
  • Forming assemblies
  • Outputting drawings
  • "Revolving"
  • "Sweeping"
  • Shape creation involving repeated "patterns"
  • Surface creation
  • Forming sheet metal objects
  • Using the "toolbox" of standard parts
  • Using "design tables"
  • Animating objects
  • Analysing the motion of animated objects

 

Further notes

There is a moodle page supporting the course. 

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 30/05/2023 15:10

Engineering Tripos Part IA, Engineering Drawing, 2023-24

Lecturer

Prof. Nathan Crilly

Timing and Structure

The course is introduced by a lecture (this will be recorded). Five self-paced workbooks must be completed. These are assessed in two scheduled mark up sessions. Helpdesk support is available prior to the scheduled mark up sessions.

Aims

The aims of the course are to:

  • demonstrate the role of engineering drawing in design and communication
  • develop skills in reading different types of engineering drawings
  • develop skills in producing different types of engineering drawings.

Objectives

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

  • read and produce orthographic projection drawings (with the correct arrangement of principal views)
  • distinguish first-angle drawings from third-angle drawings
  • read isometric drawings of simple and complex shapes
  • sketch simple shapes in isometric and combine them to generate more complex shapes
  • convert between isometric and orthographic drawings (drawing one based on the other)
  • read and produce auxiliary views (on orthographic projection drawings)
  • construct basic sequences of auxiliary views (projecting new views from the preceding views)
  • read and produce hidden detail on isometric and orthographic drawings
  • read and produce sectioning on isometric and orthographic drawings
  • construct isometric sketches from successive sections
  • read and produce isometric and orthographic drawings with basic dimensions
  • identify and correct over-dimensioning or under-dimensioning on drawings
  • read and produce drawings which account for the effects of simple dimensional variation.

Content

The course is divided into five topics, each delivered through a workbook. Each workbook provides explanations, examples and exercises, arranged into sub-topics.

1. Orthographic projection

1.1. The different kinds of drawing used on the course

1.2. Different types of lines and what they represent

1.3. How orthographic projections are constructed

1.4. The main two conventions for how orthographic projections are laid out 

1.5. The principal views which are often drawn in orthographic projections 

1.6. The reason that 2nd and 4th angle projections aren’t used (an appendix).

2. Isometric drawing

2.1. What isometric views are

2.2. How to sketch basic shapes

2.3. How to sketch circles, cylinders and spheres

2.4. How to represent locations, movements and forces

2.5. How to draw dimetric and trimetric views.

3. Auxiliary views

3.1. Identifying significant views of planes and lines

3.2. Projecting auxiliary views from principal views 

3.3. Methods for constructing auxiliary views

3.4. Projecting partial auxiliary views

3.5. Significant views of forces and moments

3.6. Considering isometric projections as auxiliary views (an appendix).

 

4. Sectioning

4.1. The presentation of hidden detail

4.2. The presentation of section views

4.3. Special rules for offset, partial, revolved, removed and successive sections 

4.4. Combining auxiliary views with section views to yield auxiliary sections 

4.5. Drawing sections in isometric views

4.6. Special rules for sectioning thin material (an appendix).

 

5. Dimensioning

5.1. Presenting measurements on drawings

5.2. Some principles of dimensioning

5.3. Problems with over-dimensioning and under-dimensioning 

5.4. Accounting for dimensional variation

 

Booklists

Please refer to the Booklist for Part IA 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: 30/05/2023 15:10

Engineering Tripos Part IA, Computer-Aided Design, 2022-23

Lecturer

Dr Richard Roebuck

Timing and Structure

This course involves: a single lecture in week 1 of Michaelmas Term; a Tutorials sheet to work though; a Tasks sheet on which you will be assessed. Help desk support is available through the term. Marking occurs at (or before) three fixed sessions.

Aims

The aims of the course are to:

  • Gain a working knowledge of Computer-aided Design (CAD) solid modelling.
  • Learn how to translate ideas, designs and real world items into shapes, assemblies and animations within a solid modelling environment.

Objectives

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

  • Use our chosen professional CAD package to create models of engineering components and assemblies.
  • Representing ideas, designs and real world items in the CAD environment in a range of ways.
  • Create output from the CAD environment, including animations, so as to be able to communicate ideas in a range of ways.

Content

The IA Computer-aided Design (CAD) course runs in Michaelmas Term and focusses on learning, and being assessed on, the operation of a professional CAD package.

The delivery of this course (lecture, helpdesks and marking sessions) are detailed on the moodle page supporting this course.

 

Michaelmas Term

  • Introduction to Solidworks
  • Creating parts
  • Forming assemblies
  • Outputting drawings
  • "Revolving"
  • "Sweeping"
  • Shape creation involving repeated "patterns"
  • Surface creation
  • Forming sheet metal objects
  • Using the "toolbox" of standard parts
  • Using "design tables"
  • Animating objects
  • Analysing the motion of animated objects

 

Further notes

There is a moodle page supporting the course. 

Examination Guidelines

Please refer to Form & conduct of the examinations.

 
Last modified: 24/05/2022 14:05

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