MFIN7034B - Machine Learning and Artificial Intelligence in Finance

Semester 2, 2022-23

Professor
Ye Luo
Syllabus Machine learning and artificial intelligence are the apex technologies of the information era.  These methods are getting increasingly popular in the financial market.  This course provides students the fundamental models and methods of machine learning and apply them to solve real-world financial problems.  The topics include regression, classification, clustering methods, model selection, topic modelling and policy search.  The first part of the course focuses on supervised learning techniques for regression and classification.  The second part of the course covers unsupervised learning techniques for clustering and matrix factorisation.  The third part of the course covers reinforcement learning algorithm.  The last part provides the fundamental concepts of artificial intelligence and its implications.  The course provides introductions to the latest datasets in financial markets and practices applying learning algorithms to these datasets in a variety of topics.  The primary mode of learning is based on assignments and projects.
Course Objectives
  1. To introduce modern AI technology and its applications in Finance.
  2. To provide real experience of applying AI technology to finance and economics problems.
  3. To understand principles of AI and its state of art modern developments.
Learning Outcomes
Course Learning Outcomes
CLO1. The students will be able to understand principles of A.I.
CLO2. The students will be able to get experience in applying AI to finance
CLO3. Group work allows the students to collaborate and produce real useful products with data and AI technology.
CLO4. The students will be mastering the status quo of developments of AI and Fintech in the greater China area.
 
Pre-requisites Nil
Compatibility Nil
Assessment
Description Type Weighting * Tentative Assessment Period /
Examination Period ^		 Course Learning Outcomes
Class Participation & Written Assignments Continuous Assessment TBC - CLO1, CLO2, CLO3, CLO4
Written exam covers all taught content in the course Written Examination TBC TBA CLO1
* 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 oblige to 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 Selected course materials will be posted on the Moodle
Session dates
Date Time Venue Remark
Session 1 7 Feb 2023 (Tue) 6:30pm - 10:00pm Rm J, Cyberport 4 Face-to-face
Session 2 10 Feb 2023 (Fri) 6:30pm - 10:00pm Rm J, Cyberport 4 Face-to-face
Session 3 14 Feb 2023 (Tue) 6:30pm - 10:00pm Rm J, Cyberport 4 Face-to-face
Session 4 17 Feb 2023 (Fri) 6:30pm - 10:00pm Rm J, Cyberport 4 Face-to-face
Session 5 21 Feb 2023 (Tue) 6:30pm - 10:00pm Rm J, Cyberport 4 Face-to-face
Session 6 24 Feb 2023 (Fri) 6:30pm - 10:00pm Rm J, Cyberport 4 Face-to-face
Session 7 28 Feb 2023 (Tue) 6:30pm - 10:00pm Rm J, Cyberport 4 Face-to-face
Session 8 3 Mar 2023 (Fri) 6:30pm - 10:00pm Rm J, Cyberport 4 Face-to-face
Session 9 7 Mar 2023 (Tue) 6:30pm - 10:00pm Rm J, Cyberport 4 Face-to-face
Session 10 10 Mar 2023 (Fri) 6:30pm - 10:00pm Rm J, Cyberport 4 Face-to-face
Add/drop 16 January, 2023 - 10 February, 2023
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