Study information

Fundamentals of Machine Learning - 2024 entry

MODULE TITLEFundamentals of Machine Learning CREDIT VALUE15
MODULE CODECOM1011 MODULE CONVENERDr Chico Camargo (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 elementary programming language's function (python) for implementing machine learning algorithms. 
 
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 specialist reports.
 

 

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;
  • Examples of 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,  clustering;
  • Algorithms: , hierarchical clustering, linear models, naïve Bayes, k-means, PCA and Dimensionality reduction; 
  • Theoretical Notions in Machine Learning: model capacity and overfitting, model complexity .
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 33 Guided Independent Study 117 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 22 Lectures
Scheduled Learning and Teaching Activities

11

 Workshops/tutorials
Guided Independent Study 117 Individual assessed work

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Workshops will have formative assessment.

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 1 30 24 hours All Written
Coursework 2 70 50 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
Coursework 1 Coursework 1 All Completed over summer with a deadline in August
Coursework 2 Coursework 2 All Completed over summer with a deadline in August

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework 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

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 Duda, R.O. and Hart, P.E. Pattern Classification 2nd Wiley 2000 978-0471056690
Set Webb, A. Statistical Pattern Recognition 2 Wiley 2002 0-470-84513-9
Set Murphy, K. Machine Learning: A Probabilistic Perspective 1st MIT Press 2012 978-0-262-018029
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 Thursday 20th June 2024 LAST REVISION DATE Tuesday 19th November 2024
KEY WORDS SEARCH Data; Machine Learning; Pattern Recognition; Probability

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