Computer Science

ECMM422 - Machine Learning (2019)

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MODULE TITLEMachine Learning CREDIT VALUE15
MODULE CODEECMM422 MODULE CONVENERDr Nicolas Pugeault (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 11 0
Number of Students Taking Module (anticipated) 27
DESCRIPTION - summary of the module content

Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering. This module will provide you with a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition.  Hence, it is particularly suitable for Computer Science, Mathematics and Engineering students and any students with some experience in probability and programming.

PRE-REQUISITE MODULES ECM3420 or ECMM445

AIMS - intentions of the module

In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of computational methods used in modern data analysis.

In particular, this module aims to impart knowledge and understanding of machine learning methods from basic pattern-analysis methods to state-of-the-art research topics; to give you experience of data-modelling development in practical workshops. Neural Networks, Bayesian methods and kernel-based algorithms will be introduced for extracting knowledge from large data sets of patterns (data mining techniques) where it is important to have explicit rules governing machine learning and pattern recognition. Recent development of techniques and algorithms for big-data analysis will also be addressed.

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. apply advanced and complex principles for statistical machine learning to various data analysis;

2. analyse novel pattern recognition and classification problems; establish statistical models for them and write software to solve them;

3. apply a range of supervised and unsupervised machine learning techniques to a wide range of real-life applications.

Discipline Specific Skills and Knowledge:

‚Äč4. state the importance and difficulty of establishing a principled probabilistic model for pattern recognition;

5. apply a number of complex and advanced mathematical and numerical techniques to a wide range of problems and domains.

Personal and Key Transferable / Employment Skills and Knowledge:

6. identify the compromises and trade-offs which must be made when translating theory into practice;

7. critically read and report on research papers;

8. conduct small individual research projects.

SYLLABUS PLAN - summary of the structure and academic content of the module

- Introductory material: Practical motivation for machine learning, basic ideas of supervised and unsupervised learning, classification, regression;

- Describing data;

- Latent descriptions: k-means, maximum likelihood; mixture models; PCA; ICA;

- Unsupervised learning: Clustering; Locality Sensitive Hashing;

- Supervised models: k-nearest neighbours, linear and non-linear regression, linear discriminant analysis, logistic regression, SVM and maximum margin classifiers;

- Loss functions and maximum likelihood estimators;

- Bayesian learning & sampling;

- Neural nets and deep learning;

- Evaluation of performance, dataset balance;

- Ensemble methods: boosting, bagging, decision trees and random forests Metric learning;

- Markov decision processes: Reinforcement learning (Q-learning).

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 30.00 Guided Independent Study 120.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning & Teaching activities 22 Lectures
Scheduled Learning & Teaching activities 8 Workshop/tutorials
Guided independent study 32 Project and coursework
Guided independent study 88

Guided independent study
(50 wider reading + 38 coursework preparation)

 

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 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework - 4 equally weighted workshop reports 100 1,000-2,000 words All 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
All Coursework (100%) All Completed over the Summer with a deadline in August
       
       

 

RE-ASSESSMENT NOTES

Referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 50% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

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 Search
Set Shawe-Taylor, J. and Cristianini, N. Kernel methods for pattern analysis Cambridge University Press 2006 521813972 [Library]
Set Christopher Bishop Pattern Recognition and Machine Learning Springer 2007 978-0387310732 [Library]
Set Webb, A Statistical Pattern Recognition 2 Wiley 2002 0-470-84513-9 [Library]
Set Kevin Murphy Machine Learning: A Probabilistic Perspective 1st MIT Press 2012 978-0-262-018029 [Library]
Set Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd Springer 2009 978-0387848570 [Library]
Set David Barber Bayesian Reasoning and Machine Learning Cambridge University Press 2012 978-0-521-51814-7 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECM3420, ECMM445
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 10 July 2018 LAST REVISION DATE Wednesday 03 July 2019
KEY WORDS SEARCH None Defined