The University of Sheffield
Department of Computer Science

COM6509 Machine Learning and Adaptive Intelligence

Summary The module is about core technologies underpinning modern artificial intelligence. The module will introduce statistical machine learning and probabilistic modelling and their application to describing real-world phenomena. The module will give you a grounding in modern state of the art algorithms that allow modern computer systems to learn from data.
Session Autumn 2023/24
Credits 15
  • Formal examination [LO1, LO2]
  • Assignment [LO3, LO4]
Lecturer(s) Dr Matthew Ellis & Dr Michael Smith
Aims This unit aims to provide a deep understanding of the fundamental technologies underlying modern artificial intelligence. In particular, it will provide a foundational understanding of
  • data science,
  • machine learning.
Learning Outcomes 

By the end of the unit, a candidate will be able to demonstrate the ability to

  1. understand probability theory and how it relates to uncertainty in modern artificial intelligence,
  2. understand when and how to implement the appropriate learning paradigm for a given application,
  3. implement a range of machine learning algorithms on simple problems,
  4. deploy appropriate machine learning algorithms to solve real-world problems.

Typical content will include:  

  • Introduction to machine learning
  • End-to-End machine learning
  • Linear regression
  • Decision trees and ensemble methods
  • Gaussian processes 
  • Automatic differentiation
  • Logistic regression
  • Neural networks
  • Unsupervised learning
  • Deep generative models
  • Other topics
Restriction The maximum number of students allowed on the module is 170.
Teaching Method There will be formal lectures and lab classes. These classes may involve computer-based problem solving, examples and MCQ. The lectures and example classes will run concurrently, covering similar topics, with the theory being laid out in the lectures, and application of the theory taking place in example classes.
Feedback Students will receive feedback on their assignments through Blackboard and in the Lab Sessions.