Overview
COM4509/COM6509 Machine Learning and Adaptive Intelligence 201516
Course Overview
This unit aims to provide an understanding of the fundamental technologies underlying modern artificial intelligence. In particular it will provide foundational understanding of probability and statistical modelling, supervised learning for classification and regression, and unsupervised learning for data exploration. The teaching consists of two hours of lectures and one of lab classes each week. The lectures are on Tuesdays, the labs on Fridays. The teaching schedule and venue for each week are given below:
Lectures

Week 1 on Sep 29, 2015 in SB LT2. Introduction to machine learning and a review of probability theory.

Week 2 9:00 on Oct 6, 2015 in SBLT2. Objective functions, gradient descent and matrix factorization.

Week 3 9:00 on Oct 13, 2015 in SBLT2. Linear algebra and regression.

Week 4 9:00 on Oct 20, 2015 in SB LT2. Basis functions.

Week 5 9:00 on Oct 27, 2015 in SB LT2. Generalisation.

Week 6 9:00 on Nov 3, 2015 in SB LT2. Bayesian regression.

Week 7 on Nov 10, 2015. Reading Week.

Week 8 9:00 on Nov 17, 2015 in SB LT2. Unsupervised Learning.

Week 9 9:00 on Nov 24, 2015 in SB LT2. Naive Bayes.

Week 10 9:00 on Dec 1, 2015 in SB LT2. Logistic Regression and Generalized Linear Models.

Week 11 on Dec 8, 2015. Reading Week.

Week 12 9:00 on Dec 15, 2015 in SB LT2. Gaussian processes.
Assessment
The unit will be assessed by submitted practical assignments (7 labs at 10% each leading to 70%) and by exam (30%).
Past Papers
Information on past papers is available.
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