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Accounting & Finance Finance and Compliance

Certificate for Module (Machine Learning in Finance)
證書( 單元 : 機器學習與金融科技)

CEF Reimbursable Course

CEF Reimbursable Course

Course Code
FN114A
Application Code
2385-FN114A

Credit
9
Study mode
Part-time
Start Date
To be advised
Next intake(s)
May 2026
Duration
4 months
Language
English
Course Fee
4,900
Deadline on 19 Sep 2025 (Fri)
Enquiries
28678312
28584750
How to Apply

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Highlights

This module provides students with the basic machine learning techniques commonly used in finance. Students will learn how to formulate and analyse finance problems from a machine learning perspective. The end-to-end process of investigating data through different machine learning algorithms in financial decision analysis will be discussed.
 

Programme Details

On completion of the programme, students should be able to
  1. Identify the fundamental issues of machine learning in terms of data and model selection;
  2. Explain the common machine learning approaches in finance;
  3. Describe the underlying mathematical models within and across machine learning algorithms and the paradigms of supervised and un-supervised learning;
  4. Design and implement different machine learning algorithms in finance.

 

Award

Upon successful completion of the programme, students will be awarded within the HKU system through HKU SPACE a “Certificate for Module (Machine Learning in Finance)”.

 

Assessment Criteria

Assessment will comprise continuous assessment (assignments) and examination. All assessments will be in English.

 

Attendance Requirement

Student are required to achieve at least 70% in attendance to complete the programme.

Application Code 2385-FN114A -

Venue

Modules

Syllabus

Basic Linear Algebra
  • Matrices and vectors
  • Addition and vector multiplication
  • Matrix multiplication properties
  • Inverse and transpose
Linear Regression
  • Model representation
  • Hypothesis representation
  • Logistic regression
  • Decision boundary
  • Optimisation
Model Selection and Regularisation
  • Model selection
  • Overfitting
  • Regularised linear regression
  • Regularised logistic regression
Neural Networks
  • Non-linear hypotheses
  • Neurons and brain
  • Model representation
Support Vector Machines
  • Optimisation and objective
  • Large margin
  • Kernels
  • Using SVM
Unsupervised Learning
  • K-means algorithm
  • Optimisation objective
  • Random initialisation
  • Number of clusters
Dimensionality Reduction
  • Principle component analysis
  • Reconstruction with compressed representation

Class Details

May 2026 Intake - Tentative timetable (TBA)

Machine Learning in Finance
Session Lecture Date Time
1 May 2026 - Aug 2026 7:00 pm - 10:00 pm
2
3
4
5
6
7
8
9
10
Exam TBA

Venue: HKU SPACE Po Leung Kuk Stanley Ho Community College (HPSHCC) Campus (at Causeway Bay) or other locations in Hong Kong Island.

Teacher Information

Dr Albert Lam

Chief Technology Officer and Chief Scientist at Fano Labs

Background

Albert received the BEng degree (First Class Honors) in Information Engineering from the University of Hong Kong(HKU), Hong Kong, in 2005 and he obtained the PhD degree at the Department of Electrical and Electronic Engineering of HKU in 2010. He was a postdoctoral scholar at the Department of Electrical Engineering and Computer Sciences of University of California, Berkeley. He was a Research Assistant Professor at the Department of Computer Science of Hong Kong Baptist University in 2012–15 and the Department of Electrical and Electronic Engineering (EEE) of HKU in 2015–17. He is now the Chief Technology Officer and Chief Scientist at Fano Labs, a deep-tech startup specializing in speech and language technologies. He also serves at a Adjunct Assistant Professor at HKU EEE and Geograhpy. He is a Croucher research fellow. He is a member of the Expert Committee of Shenzhen Artificial Intelligence Industry Association. He is an active member of IEEE. He is the founding Chair of the Social Media Subcommittee of the IEEE Computational Intelligence Society (CIS) and has also chaired some other committees in CIS. His research interests include optimization theory and algorithms, artificial intelligence, evolutionary computation, smart grids, and smart cities. He is an Associate Editor of IEEE Transactions on Evolutionary Computation, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Artificial Intelligence, and IEEE Transactions on Emerging Topics in Computational Intelligence. He is also an co-Editor-in-Chief of EAI Endorsed Transactions on Energy Web.

Fee

Application Fee

HK$150 (Non-refundable)

Course Fee
  • Course Fee : 4,900

Entry Requirements

Applicants shall have gained in the HKDSE Examination Level 2 in 5 subjects including English Language and Chinese Lauguage or equivalent.

Applicants who do not possess the above academic qualifications but are aged 21 or above with relevant work experience will be considered on individual merit.

CEF

  • The CEF Institution Code of HKU SPACE is 100
CEF Courses
CERTIFICATE FOR MODULE (MACHINE LEARNING IN FINANCE)
證書(單元:機器學習與金融科技)
COURSE CODE 33C161141 FEES $4,900 ENQUIRY 2867-8312

Continuing Education Fund

More information of application procedures: https://hkuspace.hku.hk/cef/application-procedures

Continuing Education Fund Continuing Education Fund
This course has been included in the list of reimbursable courses under the Continuing Education Fund.

Certificate for Module (Machine Learning in Finance)

  • This course is recognised under the Qualifications Framework (QF Level [4])

Apply

Application Form Download Application Form

Enrolment Method
Payment Method
1. Cash, EPS, WeChat Pay Or Alipay

Course fees can be paid by cash, EPS, WeChat Pay or Alipay at any HKU SPACE Enrolment Centres.

2. Cheque Or Bank draft

Course fees can also be paid by crossed cheque or bank draft made payable to “HKU SPACE”. Please specify the programme title(s) for application and applicant’s name. You may either:

  • bring the completed form(s), together with the appropriate course or application fees in the form of a cheque, and any required supporting documents to any of the HKU SPACE enrolment centres;
  • or mail the above documents to any of the HKU SPACE Enrolment Centres, specifying “Course Application” on the envelope. HKU SPACE will not be responsible for any loss of personal information and payment sent by mail.
3. VISA/Mastercard

Applicants may also pay the course fee by VISA or Mastercard, including the “HKU SPACE Mastercard”, at any HKU SPACE enrolment centres. Holders of the HKU SPACE Mastercard can enjoy a 10-month interest-free instalment period for courses with a tuition fee worth a minimum of HK$2,000; however, the course applicant must also be the cardholder himself/herself. For enquiries, please contact our staff at any enrolment centres.

4. Online Payment

Online application / enrolment is offered for most open admission courses (enrolled on first come, first served basis) and selected award-bearing programmes. Application fees and course fees of these programmes/courses can be settled by using "PPS by Internet" (not available via mobile phones), VISA or Mastercard. In addition to the aforesaid online payment channels, new and continuing students of award-bearing programmes with available online service, they may also pay their course fees by Online WeChat Pay, Online Alipay or Faster Payment System (FPS). Please refer to Enrolment Methods - Online Enrolment  for details.

Notes

  • If the programme/course is starting within five working days, application by post is not recommended to avoid any delays. Applicants are advised to enrol in person at HKU SPACE Enrolment Centres and avoid making cheque payment under this circumstance.

  • Fees paid are not refundable except under very exceptional circumstances (e.g. course cancellation due to insufficient enrolment), subject to the School’s discretion. In exceptional cases where a refund is approved, fees paid by cash, EPS, WeChat Pay, Alipay, cheque, FPS or PPS by Internet will be reimbursed by a cheque, and fees paid by credit card will be reimbursed to the credit card account used for payment. 

  • In addition to the published fees, there may be additional costs associated with individual programmes. Please refer to the relevant course brochures or direct any enquiries to the relevant programme team for details.
  • Fees and places on courses cannot be transferrable from one applicant to another. Once accepted onto a course, the student may not change to another course without approval from HKU SPACE. A processing fee of HK$120 will be levied on each approved transfer.
  • HKU SPACE will not be responsible for any loss of payment, receipt, or personal information sent by mail.
  • For payment certification, please submit a completed form, a sufficiently stamped and self-addressed envelope, and a crossed cheque for HK$30 per copy made payable to “HKU SPACE” to any of our enrolment centres.
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