H.F. Ting
Jolly M.Y. Cheng
This course will cover the following topics. (1) Overview of Machine Learning and Artificial Intelligence, (2) Supervised Learning, Unsupervised Learning and Reinforcement Learning, (3) Major algorithms for Supervised Learning and Unsupervised Learning with applications to Trading and Finance, (4) Basic algorithms for Reinforcement Learning with applications to optimal trading, asset management, and portfolio optimization, (5) Advanced methods of Reinforcement Learning with applications to high-frequency trading, cryptocurrency trading and peer-to-peer lending.
| Course Learning Outcomes | Relevant Programme Learning Outcome |
| CLO1. Have a general understanding of the field on Machine Learning, and be familiar with important methods and technologies in the field | PLO4, 5, 6, 7, 14, 15 |
| CLO2. Given a particular problem in trading or finance, able to choose the Machine Learning method that would be most appropriate to solve the problem | PLO1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 14, 16 |
| CLO3. Able to implement Machine Learning solutions and access their performance | PLO1, 2, 3, 6, 7, 9, 10, 11, 14, 16 |
| View Programme Learning Outcomes | |
| Course Content | No. of Hours | Course Learning Outcomes |
| Overview of Machine Learning and Artificial Intelligence | 4 | CLO1, CLO2 |
| Supervised Learning, Unsupervised Learning and Reinforcement Learning | 8 | CLO1, CLO2, CLO3 |
| Major algorithms for Supervised Learning and Unsupervised Learning with applications to Trading and Finance | 10 | CLO1, CLO2, CLO3 |
| Basic algorithms for Reinforcement Learning with applications to optimal trading, asset management, and portfolio optimization | 8 | CLO1, CLO2, CLO3 |
| Description | Type | Weighting * | Examination Period ^ | Course Learning Outcomes |
| Assignments | Continuous Assessment | 25% | - | CLO1, CLO2, CLO3 |
| Group project | Continuous Assessment | 40% | - | CLO1, CLO2, CLO3 |
| Written exam covering all taught content of the course | Written Examination | 35% | 10 - 28 May 2022 | CLO1, CLO2 |
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* The weighting of coursework and examination marks is subject to approval ^ The exact examination date uses to be released when all enrolments are confirmed after add/drop period by the Examinations Office. Students must be obliged to follow the examination schedule. Students should NOT enrol in the course if they are not certain that they will be available during the examination period. Absence from examination may result in a failure in the course. There is no supplementary examination for all MSc curriculums in the Faculty of Engineering. |
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| Date | Time | Venue | Remark | |
| Session 1 | 21 Jan 2022 (Fri) | 7:00pm - 10:00pm | Online | Zoom |
| Session 2 | 28 Jan 2022 (Fri) | 7:00pm - 10:00pm | Online | Zoom |
| Session 3 | 11 Feb 2022 (Fri) | 7:00pm - 10:00pm | Online | Zoom |
| Session 4 | 18 Feb 2022 (Fri) | 7:00pm - 10:00pm | Online | Zoom |
| Session 5 | 25 Feb 2022 (Fri) | 7:00pm - 10:00pm | Online | Zoom |
| Session 6 | 4 Mar 2022 (Fri) | 7:00pm - 10:00pm | Online | Zoom |
| Session 7 | 18 Mar 2022 (Fri) | 7:00pm - 10:00pm | Online | Zoom |
| Session 8 | 25 Mar 2022 (Fri) | 7:00pm - 10:00pm | Online | Zoom |
| Session 9 | 1 Apr 2022 (Fri) | 7:00pm - 10:00pm | Online | Zoom |
| Session 10 | 8 Apr 2022 (Fri) | 7:00pm - 10:00pm | Online | Zoom |