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Accounting & Finance FinTech and Financial Analytics

Executive Certificate in AI and Deep Learning in Quantitative Finance
行政人員證書《量化投資:人工智能與深度學習》

Course Code
EP159A
Application Code
2260-EP159A
Study mode
Part-time
Start Date
07 Dec 2024 (Sat)
Next intake(s)
Mar 2025
Duration
2 months to 3 months
Language
English
Course Fee
Course Fee: $9900 per programme (* course fees are subject to change without prior notice)
Deadline on 22 Nov 2024 (Fri)
Enquiries
2867 8331
2861 0278
Apply Now

Today and Upcoming Events

Accept new applications for the Dec 2024 intake! There will be practical classes in the computer laboratory. Our professional lecturer will discuss deep learning algorithms (e.g., Convolutional Neural Networks, Recurrent Neural Networks, and Long Short Term Memory). Also, practical AI applications in quantitative finance and trading (e.g., reinforcement learning, anomaly detection) will be covered. Welcome to your online application!

Highlights

This programme aims to provide students with knowledge about Artificial Intelligence and Deep Learning in Quantitative Finance as well as their latest developments and applications to finance and investment. It covers various learning algorithms and neural networks as well as machine intelligence to facilitate finance and investment decision making.

Programme Details

On completion of this programme, students should be able to:

  1. Identify the latest development of AI and Deep Learning in Quantitative Finance;
  2. examine common learning algorithms and neural networks to facilitate investment decision making;
  3. illustrate the learning algorithms and neural networks using computation tools;
  4. discuss the applications of AI and Deep Learning in the finance services sector.

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Teacher

(1) Mr. Ken Liu, co-founder and CTO of Datatact Ltd, a startup focus on AI, Machine Learning and Big Data analytics. He is a hands on expert in his specialized area for over 10 years.  Prior to Datatact, Ken worked at Citi, HSBC, Goldman Sachs, Deutsche Bank and Credit Suisse as Algo-Trading developer. Ken earned a Master in Computer Science from USC and a Bachelor in Computer Science from University of Warwick.

(2) Dr. Simon Yiu,  IT Department Head of a financial institution in Hong Kong, has handled many FinTech initiatives and projects, such as Algo trading, finance big data analytics, Robo-advisors and so on. Before that, he also worked for AI, and Machine learning startup as co-founder and CTO which located at a Hong Kong Science Park and participated at the University organized Entrepreneurship Center in 2010, focusing on AI, Machine Learning, Big Data analytics and Natural language processing. Furthermore, he has hands-on programming experiences in FinTech areas for over 10 years. Simon earned a Doctoral Degree in Business Administration from the City University of Hong Kong and a Master Degree in Data Science and Business Statistics from The Chinese University of Hong Kong.

Application Code 2260-EP159A Apply Online Now
Apply Online Now

Days / Time
  • Saturday, 10:00pm - 5:00pm
Venue
  • Hong Kong Island Learning Centre
  • Kowloon East Campus
  • Kowloon West Campus

Modules

Course Content

(1) Introduction to AI and Deep Learning in Quantitative Finance

  • Overview of the latest technological developments
    • Big Data and FinTech
    • Cloud computing and 5G
    • AI, Machine Learning and Deep Learning
  • Introduction to computation tools in Quantitative Finance
    • Python Programming Language
    • Scikit-learn for AI and Machine Learning
    • TensorFlow, Keras and PyTorch for Deep Learning
  • Emerging Trends in AI, Deep Learning and FinTech

 

(2) Learning Algorithms and Machine Intelligence

  • Supervised learning: penalized regression, support vector machine, k-nearest neighbor, classification and regression tree, ensemble learning, and random forest
  • Unsupervised learning: principal components analysis, k-means clustering, and hierarchical clustering
  • Reinforcement learning: deep reinforcement learning, deep Q-Learning
  • Deep learning: Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM)
  • Cognitive analytics: Natural Language Processing (NLP), Computational Linguistics
  • Algorithms on graphs: social networks, link analysis

 

(3) Applications of AI and Deep Learning in Quantitative Finance

  • Fintech Disruption: a glimpse into the future
  • Big and Alternative data powered Investment Management: stock selection (forecast combinations, feature engineering)
  • Natural Language Processing: chatbots and sentiment analysis on corporate earnings, news and social media
  • Reinforcement Learning: automated strategy development in algorithmic trading
  • Anomaly Detection: Bankruptcy Prediction and Risk Management
  • Wealth Management: Robo-advisors and the future of Digital and Virtual Banking

Assessment method:  two in-class exercises + group project presentation

Class Details

Timetable

Dec 2024 intake : 

Lecture Date Time
1 7 Dec 24 (Sat) 10:00-12:00 & 13:00-17:00
2 14 Dec 24 (Sat) 10:00-12:00 & 13:00-17:00
3 21 Dec 24 (Sat) 10:00-12:00 & 13:00-17:00
4 4 Jan 25 (Sat) 10:00-12:00 & 13:00-17:00
5 11 Jan 25 (Sat) 10:00-12:00 & 13:00-17:00

 

Remarks: Tentative timetable is subject to change, and course commencement is subject to sufficient enrollment numbers

 

Fee

Application Fee

HK$150 (Non-refundable)

Course Fee
  • Course Fee: $9900 per programme (* course fees are subject to change without prior notice)

Entry Requirements

Applicants shall hold:
a)    a bachelor’s degree awarded by a recognized University or equivalent; or
b)   an Associate Degree/ a Higher Diploma or equivalent, and have at least 2 years of relevant working experience.


Applicants with qualifications in quantitative areas (e.g., mathematics, engineering, statistics, computer science, economics, finance) are preferred. Those with other qualifications and substantial senior level work experience will be considered on individual merit.

**Please upload copy of HKID and proof of degree while applying online

Apply

Online Application Apply Now

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.