Executive Certificate in AI and Deep Learning in Quantitative Finance - HKU SPACE: FinTech and Financial Intelligence, Data Science courses
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Class arrangement during COVID-19


The COVID-19 situation may still be fluid and constantly affect class arrangements in the coming months. The health and safety of our students will always be our top priority. To ensure that students’ academic progress is not affected, the School may substitute face-to-face classes with online teaching if necessary in the event that face to-face classes cannot be held. Our respective Programme Teams will contact the students concerned with details of such arrangements as necessary. For more details on the class arrangement during COVID-19, please refer to the special announcement on the School homepage.

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Accounting & Finance FinTech and Financial Intelligence

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
1960-EP159A
Study mode
Part-time
Start Date
04 Dec 2021 (Sat)
Next intake(s)
Mar 2022
Duration
2 months to 3 months
Language
English
Course Fee
HK$9500 per programme
Apply Now
Deadline on 19 Nov 2021 (Fri)
Enquiries
2867 8331
2861 0278
Accept new application for 2021 December intake! There will be practical classes in computer laboratory.

Our professional lecturer will discuss the algorithms of deep learning (e.g., Convolution Neural Networks, Recurrent Neural Networks and Long Short Term Memory), as well as AI applications in quantitative finance and trading. Welcome for 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.

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 1960-EP159A Apply Online Now
Apply Online Now

Days / Time
  • Saturday, 1:00pm - 7:00pm
Venue
  • Kowloon Learning Centre
  • Hong Kong Island Learning Centre

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

Timetable

2021 December intake : 

Lecture Date Time
1 4 Dec 21 (Sat) 13:00 - 19:00
2 11 Dec 21 (Sat) 13:00 - 19:00
3 18 Dec 21 (Sat) 13:00 - 19:00
4 8 Jan 22 (Sat) 13:00 - 19:00
5 15 Jan 22 (Sat) 13:00 - 19:00

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

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

Application Fee

HK$150 (student only needs to pay one time application fee for all EC in Big Data Series)

Course Fee
  • Course Fee : HK$9500 per programme (Course fees are subject to change without prior notice)
  • Early Bird Rate : HK$8900 per programme (Early-Bird discounted fee for enrolment on/before 21 May 21)
  • Alumni Rate : HK$8900 per programme (Alumni from EDEC in Big Data and FinTech Programme Series)

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.

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 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.