COMP7409B - Machine learning in trading and finance

Summer Semester, 2022-23

Professor
H.F. Ting
Teaching assistants
Jolly M.Y. Cheng
Chun Ming Chow
Syllabus The course introduces our students to the field of Machine Learning, and help them develop skills of applying Machine Learning, or more precisely, applying supervised learning, unsupervised learning and reinforcement learning to solve problems in Trading and Finance.

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.
Introduction by Professor -
Learning Outcomes
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
Pre-requisites Experience with Python programming, and some basic knowledge of probability theory and linear algebra
Compatibility -
Topics covered
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
 
Assessment
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% 7 - 19 Aug 2023 CLO1, CLO2
* 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 are obliged to follow the examination schedule.  Students should NOT enrol in the course if they are not certain that they will be in Hong Kong during the examination period.  Absent from examination may result in failure in the course. There is no supplementary examination for all MSc curriculums in the Faculty of Engineering.
Course materials -
Session dates
Date Time Venue Remark
Session 1 14 Jun 2023 (Wed) 7:00pm - 10:00pm CYP-P3 Face-to-face
Session 2 21 Jun 2023 (Wed) 7:00pm - 10:00pm CYP-P3 Face-to-face
Session 3 28 Jun 2023 (Wed) 7:00pm - 10:00pm CYP-P3 Face-to-face
Session 4 5 Jul 2023 (Wed) 7:00pm - 10:00pm CYP-P3 Face-to-face
Session 5 7 Jul 2023 (Fri) 7:00pm - 10:00pm CYP-P3 Face-to-face
Session 6 12 Jul 2023 (Wed) 7:00pm - 10:00pm CYP-P2 Face-to-face
Session 7 14 Jul 2023 (Fri) 7:00pm - 10:00pm CYP-P2 Face-to-face
Session 8 19 Jul 2023 (Wed) 7:00pm - 10:00pm CYP-P2, CPD-2.37 & CPD-2.42 Face-to-face
Session 9 21 Jul 2023 (Fri) 7:00pm - 10:00pm CYP-P2, CPD-2.37 & CPD-2.42 Face-to-face
Session 10 26 Jul 2023 (Wed) 7:00pm - 10:00pm CYP-P2 Face-to-face
CYP - Chong Yuet Ming Building
Add/drop 12 June, 2023 - 21 June, 2023
Maximum class size 150
Moodle course website
  • HKU Moodle: https://moodle.hku.hk/course/view.php?id=102614 (Login using your HKU Portal UID and PIN)

    - Please note that the professor maintains and controls when to release the Moodle teaching website to students.
    - Enrolled students should visit the Moodle teaching website regularly for latest announcements, course materials, assignment submission, discussion forum, etc.
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