Computer Science

COM2011 - Machine Learning and Data Science (2019)

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MODULE TITLEMachine Learning and Data Science CREDIT VALUE15
MODULE CODECOM2011 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 12 0 0
Number of Students Taking Module (anticipated)
DESCRIPTION - summary of the module content

This module will improve your knowledge and skills in machine learning and data science. You will gain theoretical and practical understanding of some of the core techniques in machine learning (including supervised/unsupervised methods, feature extraction, binary classification, elementary text and image analysis, amongst others). You will also understand how machine learning and other techniques are combined in effective data science workflows, alongside some of the practical challenges faced in real-world data science, such as handling missing or erroneous data, linking different datasets, and data visualisation.

Pre-requisites: COM1011 Fundamentals of Machine Learning, ECM1400, ECM1410, MTH1002, MTH1004, or equivalent

Co-requisites: MTH2006

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 fundamentals of machine learning and data analysis. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods. We will pay particular attention to methods for visualising complex datasets.

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 Analyse a broad range of data science problems, design models and write programs to solve them;

2 Utilise a range of supervised and unsupervised pattern recognition and machine learning techniques to solve a variety of problems;

Discipline Specific Skills and Knowledge:

3 State the challenges and limitations entailed by various machine learning approaches;

4 Propose the most suited analysis tools for specific data and problems;

Personal and Key Transferable/ Employment Skills and Knowledge:

5 Identify the compromises and trade-offs 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

Outline of the topics covered in this module:

• Gradient-based optimisation;

• Error and loss functions;

• Decision Trees and Random Forests;

• Ensemble methods;

• PCA;

• Deep Neural Networks, convolutional architectures;

• Support Vector Machines and large margin classification;

• Dimension reduction (forward & backward elimination).

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 36.00 Guided Independent Study 114.00 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 12 Workshops
Guided Independent Study 50 Coursework
Guided Independent Study 64 Supplementary Reading and Study

 

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
Not Applicable      

 

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 2-6 Oral on request
Coursework 1 20 25 hours 1, 2, 4 Written
Coursework 2 20 25 hours 1, 2, 4 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 (60%) 2-6 August Ref/Def Period
Coursework Coursework (40%) 1, 2, 4 Summer with 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 Webb, A. Statistical Pattern Recognition 2 Wiley 2002 0-470-84513-9 [Library]
Set Hastie T., Tibshirani R. & Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd Springer 2009 978-0387848587 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECM1400, ECM1410, MTH1002, MTH1004, COM1011
CO-REQUISITE MODULES MTH2006
NQF LEVEL (FHEQ) 5 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Friday 12 April 2019 LAST REVISION DATE Monday 19 August 2019
KEY WORDS SEARCH Data Science; Machine Learning; Pattern Recognition