COMP219: Advanced Artificial Intelligence (2020/2021)
Every week, I will try to arrange a one-hour office time, but won't be a fixed time. So the following timeslot will be subject to change. However, please send an email to me to ensure my availability before coming.
- Thursday 13:30 -- 14:30 through Zoom link (Meeting ID: 918 8149 4023; Passcode: 4X!P8y^s)
- 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;
- 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
- two assignments (15% each)
- final exam (70%)
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.
Please follow the lab sessions available at Canvas for tutorial materials. In addition to the lab materials, you could follow the "Python Machine Learning" book.
For the installation of tensorflow or other software package, it is recommended that you do this through virtual envionrments, which may be able to go around the user priviledge. See https://www.tensorflow.org/install/pip#2-create-a-virtual-environment-recommended
Also, if needed, please install Jupyter Notebook. Please read its document to understand what it is.
There will be two project assignments, which will be disclosed in due course. Each coursework assignment takes 15% of the final mark.
The assignments will also be made available at Canvas.
For general game design and implementation, the following books are recommended:
- 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.