COM4509 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
Students should be aware that there are limited places available on this module.
- Formal examination [LO1, LO2]
- Assignment [LO3, LO4]
||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,
- machine learning.
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,
- implement a range of fundamental and/or advanced machine learning algorithms on simple problems,
- deploy appropriate machine learning algorithms to solve real-world problems.
- Introduction to machine learning
- End-to-End machine learning
- Linear regression
- Decision trees and ensemble methods
- Automatic differentiation
- Logistic regression
- Neural networks
- Unsupervised learning
- Deep generative models
- Other topics
||The maximum number of students allowed on the module is 40.
||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.
||Students will receive feedback on their
assignments and quizzes through Blackboard 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
- A. Geron, Hands-On Machine Learning with Scikit-Learn & TensorFlow
- I. Goodfellow, Y. Bengio, A. Courville. Deep learning. MIT press
- Flach, Peter. Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press, 2012.
- Zaki, Mohammed J., and Wagner Meira Jr. Data Mining and Machine Learning: Fundamental Concepts and Algorithms. Cambridge University Press, 2020.