MSc Machine Learning

Year of entry: 2025

Course unit details:
Advanced Topics in Machine Learning

Course unit fact file
Unit code COMP64802
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Available as a free choice unit? Yes

Overview

A key property of real-world data is that it is often presented in large dimensions and yet many of its relevant properties can be encoded in much lower dimensions. The ability to find such low dimensional representation of data can hugely aid the feasibility of the downstream Machine Learning (ML) tasks that one might want to accomplish using it. Further, real-world data is often unlabelled. In this module we will learn to discover such efficient representations of unlabelled data and learn how it can be leveraged to extract insights. 

Pre/co-requisites

Unit title Unit code Requirement type Description
Topics in Machine Learning COMP64501 Pre-Requisite Recommended

Aims

There are two primary aims of this module (a) to introduce the students to thinking systematically about unsupervised machine learning with high-dimensional data and (b) to get the students become familiar with implementing on modern software some of the classical algorithms of this type. 

Learning outcomes

  1. Describe the fundamental concepts of unsupervised learning and in particular the methods of detecting effective low dimensional descriptions of data, if such exist.  
     
  2. Analyze the differences between the methods and utility of various ML methods that detect effective low dimensional structure in data.
     
  3. Explain the models and training algorithms used to implement a (Variational)Auto-Encoder (V)AE. 
     
  4. Apply different dimension reduction methods in a judicious way depending on the data and the need. 
     
  5. Implement different unsupervised ML methods in standard ML-specific software. 

     

Syllabus

  • Principal Component Analysis (and its variants)
  • Spectral Clustering
  • Dimension Reduction  
  • Boltzmann Machines
  • (Variational) Auto-Encoders  

Teaching and learning methods

  • Asynchronous material in the form of video lectures, formative exercises, lecture slides and code examples delivered via the virtual learning environment, lectures and supported tutorials.  
     
  • Weekly lectures consolidating asynchronous materials and providing opportunities for discussion and questions.  
     
  • Weekly supervised lab sessions provide support for coursework and formative exercises.   

Employability skills

Analytical skills
Innovation/creativity
Problem solving
Written communication

Assessment methods

Method Weight
Other 50%
Written exam 50%

Other: Coursework (50%) 
 

Feedback methods

  • Individual feedback would be available on request when coursework scores are released.  
     
  • Cohort level feedback will be available for the final exam, after marking.  

Recommended reading

“Introduction to Statistical Learning”, 
G. James, D. Witten, T. Hastie, R. Tibshirani, Springer, 2023.  
 

“Probabilistic Machine Learning: Advanced Topics”,  
K.P. Murphy, MIT Press, 2023. 

Study hours

Scheduled activity hours
Assessment written exam 2
Lectures 10
Tutorials 20
Independent study hours
Independent study 118

Teaching staff

Staff member Role
Anirbit Mukherjee Unit coordinator

Additional notes

Students are expected to spend 20 hours on Coursework.

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