COM3240 Adaptive Intelligence (10 credits)

Year Running: 2015/2016

Credit level: F6

Additional Information

COMU101, COMU103, COMU06, COMU05, COMU117, COMU109, COMU118. Students from departments other than Computer Science will need to demonstrate an excellent understanding of programming (Python or Matlab) and mathematics. A level math is compulsory.

Description

This module aims to teach students the theory and implementation of bio-inspired machine learning algorithms. Topics 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). As well as the material taught in class, students are expected to self-study relevant books and research articles and produce reports in research article styles.

Reading List

Please click here for reading list.

Teaching Methods

Delivery Type	Hours
Independent	64.0
Lab	12.0
Lecture	24.0

Methods of assessment

Assessment Type	Duration	% of formal assessment	Semester
Course Work	0.0	100 %	S2

Teaching methods and assessment displayed on this page are indicative for 2023-24.