Computer Science

COM1011 - Fundamentals of Machine Learning (2019)

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MODULE TITLEFundamentals of Machine Learning CREDIT VALUE15
MODULE CODECOM1011 MODULE CONVENERDr Leon Danon (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 0 0
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content

Differently from traditional software, artificially intelligent software can improve performance upon ingesting increasing quantities of data. This module will introduce you to the core concepts that are needed to understand the field of Artificial Intelligence and Machine Learning. You will learn about the principal paradigms from a theoretical point of view and gain practical experience through a series of workshops. In this module we will emphasize the notion and importance of data and you will learn how machines can deal with different types of data sources, ranging from images and text to networks and user preferences.

Co-requisite Modules: ECM1400, MTH1002, MTH1004, or equivalent.

This module is suitable for students with sufficient preparation in Mathematics and Programming.

AIMS - intentions of the module

This module aims to equip you with the fundamental notions to understand and identify the compromises and trade-offs that must be made when using a machine learning approach. It will provide the foundations to understand the principal flavours of machine learning techniques. Emphasis will be placed on how to work effectively with different information sources.

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 Understand and identify the compromises and trade-offs that must be made when using a machine learning approach;

2 Analyse problems from a data-centric point of view, choose among a range of supervised and unsupervised machine learning techniques and use relevant software libraries to solve them;

Discipline Specific Skills and Knowledge:

3 State the importance and difficulty of establishing machine learning solutions;

4 Use a modern programming language for scientific analysis and simulation;

Personal and Key Transferable/ Employment Skills and Knowledge:

5 Identify the compromises that must be made when translating theory into practice;

6 Critically read and report on research papers.

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

- Introductory Material: history of Artificial Intelligence and Machine Learning;

- Data: the nature of data, how to represent data sources: text, sound, images, networks;

- AI and ML applications to real world cases;

- Data Representation: feature selection, feature construction;

- Machine Learning Paradigms: supervised, unsupervised, reinforcement learning;

- Error Measures for Different Machine Learning Tasks: classification, regression, ranking, clustering;

- Algorithms: k-nearest neighbours, linear models, naïve Bayes, k-means, neural networks;

- Theoretical Notions in Machine Learning: model capacity and overfitting, curse of dimensionality.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 50.00 Guided Independent Study 100.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 24 Lectures
Scheduled Learning and Teaching Activities 26 Workshops/tutorials
Guided Independent Study 50 Individual assessed work
Guided Independent Study 50 Individual assessed work

 

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
Workshops will have formative assessment       
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 40 Written Exams 60 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 – Closed Book 60 2 hours – Winter Exam Period All Oral on request
Coursework 1 20 25 hours All Written
Coursework 2 20 25 hours 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 Above Written exam (70%) All August Ref/Def Period
All Above Coursework (30%) All Completed over 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 40% 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

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Bishop, C. Pattern Recognition and Machine Learning 1 Springer 2006 978-0387310732 [Library]
Set Duda, R.O. and Hart, P.E. Pattern Classification 2nd Wiley 2000 978-0471056690 [Library]
Set Webb, A. Statistical Pattern Recognition 2 Wiley 2002 0-470-84513-9 [Library]
Set Murphy, K. Machine Learning: A Probabilistic Perspective 1st MIT Press 2012 978-0-262-018029 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 6 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Friday 12 April 2019 LAST REVISION DATE Monday 02 December 2019
KEY WORDS SEARCH Data; Machine Learning; Pattern Recognition; Probability