The University of Sheffield
Department of Computer Science

COM3240 Adaptive Intelligence

Summary This course will examine the theme of bio-inspired Machine Learning. The students will be taught supervised, unsupervised and reinforcement learning methods in Neural Networks, including state-of-the-art algorithms (Deep Reinforcement Learning), as well as their underpinning mathematical principles and biological inspiration.
Session Spring 2021/22
Credits 10
Assessment
  • Assignments
  • Exam
Lecturer(s) Dr Matthew Ellis
Resources
Aims To teach students the theory and implementation of bio-inspired machine learning algorithms.
Objectives By the end of the course, students should:
  • understand the theory, implementation and application of supervised, unsupervised and reinforcement learning algorithms
  • be able to read relevant textbooks and research articles
  • be able to write research reports
Content Topics to include:
  • Supervised learning: the backpropagation algorithm.
  • Learning and Memory in Brain Circuits and Artificial Neural Networks
  • Unsupervised Learning (e.g. Oja's rule/Principal Component Analysis, Clustering/Competitive Learning)
  • Reinforcement Learning (e.g. Temporal Difference Learning: Q learning, SARSA, and Deep Reinforcement Learning).
Restrictions
Students from departments other than Computer Science will need to demonstrate a good understanding of programming and mathematics
Teaching Method The course will be delivered as a series of pre-recorded video lectures that students are required to watch prior to a weekly 1 hour interactive session and a 1 hour practical lab session. These will be backed up by exercises and homework. Group discussion of issues relevant to the course will be encouraged. Self-study of the course is also required, supported by a variety of resources (e.g. journal articles, books). Knowledge of programming (Matlab or Python) and A level maths is compulsory.
Feedback Students will receive feedback on assignments in writing via Blackboard and via an in-class feedback session. Feedback will also be delivered during labs and office hours (upon request).
Recommended Reading

A selection from: