COMP219: Advanced Artificial Intelligence (2022/2023)
Staff
Aims
- To equip students with the knowledge about basic algorithms that have been used to enable the AI agents to conduct the perception, inference, and planning tasks;
- To equip students with the knowledge about machine learning algorithms;
- To provide experience in applying basic AI algorithms to solve problems;
- To provide experience in applying machine learning algorithms to practical problems;
Learning Outcomes
- Ability to explain in detail how the techniques in the perceive-inference-action loop work
- Ability to choose, compare, and apply suitable basic learning algorithms to simple applications
- Ability to explain how deep neural networks are constructed and trained, and apply deep neural networks to work with large scale datasets
- Understand reinforcement learning, and is able to develop deep reinforcement learning algorithms for suitable applications
Assessment
- two assignments (30% in total, in a single submission)
- final exam (70%)
Lectures
There will be 26-30 lectures. The lecture notes will appear here one week ahead of the lectures. Please use Canvas to access the lecture information.
Tutorials:
Please follow the lab sessions available at Canvas for tutorial materials. The software code is available at the module GitHub site.
Assignments:
The assignments will also be made available at Canvas.
TextBooks:
The textbook for this module is Machine Learning Safety , which will be published soon.
Other than the textbook, the following books are recommended for reading:
- Christopher Bishop, Pattern Recognition and Machine Learning, Springer-Verlag New York, 2006.
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press. 2017
- Sebastian Raschka. Python Machine Learning. Packt Publishing. 2016.