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Accounting & Finance FinTech and Financial Intelligence

Executive Certificate in AI and Deep Learning in Quantitative Finance

Course Code
Application Code
Study mode
Start Date
07 Dec 2024 (Sat)
Next intake(s)
Mar 2025
2 months to 3 months
Course Fee
Course Fee: $9900 per programme (* course fees are subject to change without prior notice)
Deadline on 22 Nov 2024 (Fri)
2867 8331
2861 0278
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Today and Upcoming Events

Aug 2024

Online Executive Certificate / Diploma Information Seminar - Big Data & FinTech Series (19 Aug 2024)

The recent advances in Big Data and AI have major impact on the investment and trading community.  Now different types of alternative data from news, social sentiment to satellite images can be used to construct and manage investment portfolios. Moreover, Machine Learning is applied to stock price predictions while Reinforcement Learning (Alpha-Go) technique is employed into trading strategies discovery. This programme is suitable for degree holders and Executives who wish to enhance their knowledge and current market practices in the Big Data and FinTech series. Seminar topics: Course details, entry requirements, assessment requirements. This information seminar provides details about: -Executive Diploma in Financial Analytics  行政人員文憑《金融數據分析》 -Executive Certificate in Banking and Financial Technology  行政人員證書《銀行及金融科技》 -Executive Certificate in Big Data and Business Analytics  行政人員證書《大數據與業務分析》 -Executive Certificate in Big Data and Predictive Analytics  行政人員證書《大數據與預測分析》 -Executive Certificate in Big Data, A.I. and Investing  行政人員證書《大數據,人工智能與投資》 -Executive Certificate in Applications of Blockchain in Financial Technology  行政人員證書《區塊鏈在金融科技的應用》 -Executive Certificate in Applied AI and Predictive Analytics for Business  行政人員證書《應用人工智能與商業預測分析》 -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》 -Executive Certificate in Applied Business Analytics and Decision Optimization  行政人員證書《應用商業分析與決策優化》 -Executive Certificate in Interpretation and Visualization of Business Big Data  行政人員證書《商業大數據視覺化及資訊演繹》 -Executive Certificate in Financial Decision Making: Big Data and Machine Learning  行政人員證書《財務決策:大數據及機器學習》 -Executive Certificate in Text Analytics and NLP with Financial Technology 行政人員證書《金融科技:文字分析與自然語言處理》 -Certificate for Module (Big Data Governance and Data Compliance)  證書(單元 : 大數據治理及數據合規) -Certificate for Module (Business Analytics and Web Scraping)  證書(單元 : 商業分析及網站擷取) -Certificate for Module (Robotic Process Automation with Business and Financial Applications)  證書(單元:機械人流程自動化於商業與財務應用) -Certificate for Module (Distributed Ledger and Blockchain with Business Applications)  證書(單元 : 分散式帳本與區塊鏈的商業應用) -Certificate for Module (Business Intelligence and Data Automation)  證書(單元 : 商業智能與數據自動化) -Certificate for Module (Business Process Automation with VBA and Python)  證書(單元:商業流程自動化 – VBA及Python) -Certificate for Module (Business Forecasting and Predictive Analytics for Financial Decision Making)  證書(單元:財務決策的商業分析與預測) -Certificate for Module (Technical Analysis and Data Analytics for Stock Investment) 證書(單元 : 股票投資的數據與技術分析) -Certificate for Module (Sustainable Finance and Green FinTech) 證書(單元 : 可持續金融與綠色金融科技) -Certificate for Module (Generative AI, DeFi and Risk Governance) 證書(單元 : 生成式人工智能、去中心化金融與風險管治) -Certificate for Module (Web 3.0 and FinTech) 證書(單元 : 第三代互聯網與金融科技) -Certificate for Module (GenAI and Automation for Finance and Business) 證書(單元 : 生成式人工智能及金融與業務自動化) -Certificate for Module (Financial Data Analytics with Python and Power BI) 證書(單元 : 金融數據分析–Python 及Power BI) -Certificate for Module (AI and ML with Business and Financial Applications) 證書(單元 : 人工智能與機器學習 - 商業與財務應用) -Certificate for Module (Financial Informatics and Data Analytics) 證書(單元 : 金融信息學與數據分析) -Certificate for Module  (Web Application Programming for Finance and Business) 證書(單元:金融與商業網頁應用編程) Unable to join us at the Information Seminar? Email to for One-on-One after-office-hour consultation, by appointment only.

Sep 2024

Web3 and Blockchain VIII (2 Sep 2024)

Join us for an exhilarating online talk show titled "Web3 and Blockchain VIII".    This session is designed to take you on a journey through the transformative landscape of blockchain technology, which is at the heart of a decentralized digital ecosystem—a significant leap from the interactive platforms of Web2.   Bitcoin ETF Approval by the SEC: The crypto community is abuzz with the news that the U.S. Securities and Exchange Commission (SEC) has given the green light to a Bitcoin ETF. This approval is expected to drive significant institutional investment into the cryptocurrency market, potentially boosting Bitcoin's price and bringing it further into the financial mainstream.   Hong Kong's Push for Virtual Asset Regulations: In a recent move, Hong Kong has unveiled a comprehensive regulatory framework for virtual assets, aiming to bolster investor protection and foster innovation. The new guidelines provide clear rules for cryptocurrency exchanges and digital asset service providers, signaling Hong Kong's commitment to becoming a leading hub for blockchain and fintech development.   China's Digital Yuan Gains Traction: China's central bank digital currency, the digital yuan, is gaining significant traction as more businesses and consumers adopt it for everyday transactions. This development is seen as a major step towards mainstream acceptance of digital currencies and is influencing global financial systems to consider their own digital currency initiatives.   Hong Kong Steps Boldly into the Cryptocurrency Spotlight: In an exciting development, Hong Kong has given initial approval to its first bitcoin and ether spot ETFs. This pioneering move paves the way for a broader acceptance of cryptocurrencies within the region’s mainstream financial systems, underscoring a significant shift towards more regulated and accessible digital asset investments.   Bitcoin Halving Event: The crypto community is abuzz as the Bitcoin halving is done; this is a critical event that historically triggers a surge in Bitcoin's price by reducing the reward for mining new blocks by half. This anticipated change is stirring discussions about potential impacts on market dynamics and investment strategies.   Innovative Regulatory Sandbox by HKMA: In a stride towards fostering innovation while ensuring security, the Hong Kong Monetary Authority (HKMA) is set to introduce a "sandbox" for entities interested in issuing stablecoins. This initiative will allow participants to test their business models and risk management systems under manageable conditions, enhancing investor protection and promoting responsible business practices in the digital currency sphere.   Join Our Captivating Session to delve deeper into the essence of Web3 and blockchain technologies with us. Discover how these advancements are reshaping the internet and what they mean for the future of finance, privacy, and online interaction.   This is more than just a talk – it's an opportunity to be part of the next digital revolution.    Register now and be part of this transformative journey into Web 3.0.   Language: Cantonese (Supplemented with English)

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!


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.




(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
  • Hong Kong Island Learning Centre
  • Kowloon East Campus
  • Kowloon West Campus


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


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



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


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 the 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 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 (course 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, continuing students of award-bearing programmes, if their programmes offer online service, may also pay their course fees by Online WeChat Pay, Online Alipay and Faster Payment System (FPS). Please refer to Enrolment Methods - Online Enrolment  for details.


  • 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 Enrolement Centres and avoid making cheque payment under this circustance.
  • 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 or PPS (for online payment only) will normally be reimbursed by a cheque, and fees paid by credit card will normally be reimbursed to the payment cardholder's credit card account.
  • 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.
  • Receipts will be issued for fees paid but HKU SPACE will not be repsonsible for any loss of receipt 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.