Study information

Machine Learning and AI - 2024 entry

MODULE TITLEMachine Learning and AI CREDIT VALUE15
MODULE CODECOM3023 MODULE CONVENERDr Zeyu Fu (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 12 weeks 0
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content

This module will explore machine learning and artificial intelligence at an advanced level.  It examines some of the theoretical foundations of machine learning and AI, together with some advanced techniques, e.g. neural networks. In particular it examines Bayesian theory for dealing with uncertainty, and their relations to inference methods and information theory.  It also introduces techniques for dealing with temporally or spatially structured data using hidden Markov models, and reinforcement learning.

 

AIMS - intentions of the module

The aim of this module is to provide a strong theoretical basis for machine learning methods that you have already encountered and to introduce new methods for connected data and reinforcement learning.  It aims to build on and enhance your analytical skills, and to put into practice methods for new machine learning and AI paradigms.

 

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

 

Module Specific Skills and Knowledge

1. Demonstrate the theoretical foundations of machine learning and AI methods;
2. Choose appropriate analysis methods for new problems; 
3. Understand the principles underlying different machine learning and AI techniques;
4. Understand principles of machine learning and AI for spatially and temporally connected models;
5. Understand the principles and practice of reinforcement learning systems.

 

Discipline Specific Skills and Knowledge

6. Describe and compare different theoretical approaches to a single problem;
7. Learn a variety machine learning and AI methods and apply them to real problems.

 

Personal and Key Transferable / Employment Skills and Knowledge

8. Plan and write a technical report;
9. Adapt existing technical knowledge to learning new methods.
 
 
SYLLABUS PLAN - summary of the structure and academic content of the module

Indicative syllabus plan; precise content may vary from year to year.

  • Bayesian methods: theoretical perspectives; conjugate families; Monte Carlo sampling methods; approximations including Laplace approximations, variational approximation, expectation propagation.
  • Neural Networks: Introduction to deep learning, artificial neural network (multi-layer perceptron), non-linear activation functions, gradient descent and backpropagation
  • Information theory: information, entropy; coding; learning from an information theoretic point of view.
  • Learning in spatially and temporally connected models: Hidden Markov models; Markov Random Fields.
  • Reinforcement learning: Multi-armed bandits; finite Markov decision processes; temporal difference learning; on and off policy learning.

 

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 35 Guided Independent Study 115 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching 20 Lectures
Scheduled Learning and Teaching 15 Workshops and tutorials
Guided Independent Study 115 Coursework, private study, reading

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 30 Written Exams 70 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Written exam 70 2 hours 1, 2, 3, 4, 5, 6 Orally on request
Technical exercise and report 30 30 hours 1, 2, 3, 4, 5, 6, 7, 8, 9 Written
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Written Exam Written exam (2 hours) 1-6 August Ref/Def period
Technical Exercise and Report 1  Technical Exercise and Report  All August Ref/Def period

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework and/or written exam in the failed or deferred element only. For referred candidates, the module mark will be capped at 40%. For deferred candidates, the module mark will be uncapped.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading: 

ELE: http://vle.exeter.ac.uk/



Web based and Electronic Resources:

 

Other Resources:

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Bishop, C. Pattern Recognition and Machine Learning 1 Springer 2006 978-0387310732
Set Russell, S. and Norvig, P. Artificial Intelligence: A Modern Approach 4 Pearson 2016 978-1292153964
Set Mackay, D.J.C. Information Theory, Inference, and Learning 1 Cambridge 2006 978-0521642989
Set Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2 Springer 2017 978-0387848570
Set Sutton, R.S., Barto, A. and Bach, F. Reinforcement Learning: An Introduction 2 MIT Press 2018 978-0262039246
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES MTH2006, COM2011
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 6 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Friday 12th April 2019 LAST REVISION DATE Wednesday 24th July 2024
KEY WORDS SEARCH None Defined

Please note that all modules are subject to change, please get in touch if you have any questions about this module.