COM6509 Machine Learning and Adaptive Intelligence
|| 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 students a grounding in modern state of the
art algorithms that allow modern computer systems to learn
40% Formal examination
60% Coursework, consisting of assignments and quizzes.
||Dr Mauricio Alvarez & Dr Haiping Lu
|| This unit aims to provide a deep understanding of the
fundamental technologies underlying modern artificial
intelligence. In particular, it will provide a foundational
- data science,
- probability in artificial intelligence.
|| By the end of the unit, a candidate will be able to
demonstrate the ability to
- understand of probability theory and how it relates
to uncertainty in modern artificial intelligence,
- understand when and how to implement the appropriate
learning paradigm for a given application,
- have a deep understanding (including how to
implement) of a range of supervised learning algorithms.
Potential examples include: linear regression, linear
classification, naive Bayesian classification, principal
component analysis, k-means clustering, decision trees.
- have a broad overview and basis for understanding more
complex technologies, such as: the support vector
machine, kernel methods, probabilistic graphical models,
E-M algorithms, factor analysis, nonparametric Bayesian
- Probability theory
- Objective function
- Linear regression
- Basis functions
- Bayesian regression
- Unsupervised learning
- Probabilistic Classification
- Logistic Regression and Generalised Linear Models
- Other topics
||There will be 10 hours of formal lectures and 20 hours of 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.
|| Students will receive feedback on their
assignments and quizzes through MOLE and in the Lab Sessions.
- S. Rogers and M. Girolami, A First Course in Machine Learning
- C. Bishop, Pattern Recognition and Machine Learning
- D. Barber, Bayesian Reasoning and Machine Learning
- R. Duda, P. Hart and G. Stork, Pattern Classification