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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
2265-FN114A

Credit
9
Study mode
Part-time
Start Date
27 Jan 2025 (Mon)
Next intake(s)
Sep 2025
Duration
4 months
Language
English
Course Fee
4,900
Deadline on 07 Dec 2024 (Sat)
Enquiries
28678312
28584750
Apply Now

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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 2265-FN114A Apply Online Now
Apply Online Now

Modules

Syllabus

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

Class Details

Jan 2025 Intake - Tentative timetable (TBA)

Machine Learning in Finance
Session Lecture Date Time
1 22 Jan 2025 (Wed) 7:00 pm - 10:00 pm
2 5 Feb 2025 (Wed) 7:00 pm - 10:00 pm
3 12 Feb 2025 (Wed) 7:00 pm - 10:00 pm
4 19 Feb 2025 (Wed) 7:00 pm - 10:00 pm
5 26 Feb 2025 (Wed) 7:00 pm - 10:00 pm
6 5 Mar 2025 (Wed) 7:00 pm - 10:00 pm
7 26 Mar 2025 (Wed) 7:00 pm - 10:00 pm
8 29 Mar 2025 (Sat) 2:00 pm - 5:00 pm
9 2 Apr 2025 (Wed) 7:00 pm - 10:00 pm
10 9 Apr 2025 (Wed) 7:00 pm - 10:00 pm
Exam TBA

 

Venue: HKU SPACE Po Leung Kuk Stanley Ho Community College (HPSHCC) Campus (at Causeway Bay) or other locations in Hong Kong Island. The above tentative timetable may be subject to change without prior notice.

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
new 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

Online Application Apply Now

Application Form Download Application Form

Enrolment Method
  1. Please complete APPLICATION FORM and submit them in person at any of the following SPACE enrolment centre. Please refer to the SPACE enrolment centre for opening hours and address.
  2. All applications must be accompanied by:
    a) Photostat copies of full educational certificates and transcripts.
    b) Testimonials or other documentary proof of the applicant's working experience.
    c) A separate non-refundable crossed cheque payable to “HKU SPACE” for HK$150 in respect of the application processing fee. Application fee and course fee can be paid by credit cards at HKU SPACE Enrolment Centres.

Note: When submitting your application in person at any of the HKU SPACE enrolment centres, please bring along the originals of your educational certificates / transcripts and documentary proof of working experience for certification at the enrolment centres. Late applications may only be considered at the discretion of the Course Director.

Payment Method
  1. 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, student's name, and student card no. (if applicable). 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. Please refer to the SPACE enrolment centre for opening hours and address.
  2. VISA/MasterCard
    Applicants may also pay the course fee by VISA or MasterCard, including “HKU SPACE MasterCard”, at any HKU SPACE enrolment centres. Holders of HKU SPACE MasterCard can enjoy a 10-month interest-free instalment for courses with a tuition fee of HK$2,000 to $40,000. The cardholder must also be the course applicant himself/herself. For enquiries, please contact our staff at any of the enrolment centres.

Notes

  1. Fees paid are not refundable except under very exceptional circumstances, subject to the School's discretion.
  2. 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 of the relevant subject area for details.
  3. Fees and places on courses cannot be transferred 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 $120 will be levied on approved transfers.
  4. Receipts will be issued for fees paid but HKU SPACE will not be responsible for any loss of receipt sent by mail.
  5. For additional copies of receipts, please send a stamped, self-addressed envelope with a completed form and a crossed cheque for $30 per copy made payable to “HKU SPACE”. Such copies will normally only be issued at the end of a course.
  6. Classes are expected to be held at HKU SPACE learning centers. The health and safety of our students is our top priority. If the situation in Hong Kong requires. HKU SPACE reserves the right to move some classes to other locations, including online teaching platforms.
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