Course Code: FIN525
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Synopsis

FIN525 Machine Learning, Deep Learning and Applications in Finance aims to equip the students with model constructing and programming skills so that they can have a good understanding of the trending machine learning (ML) and deep learning (DL) techniques used in the financial industries. It introduces the concepts as well as fundamentals of classic algorithms and models in machine learning and deep learning. During the course, various financial data types and applications will be covered. It will help the students learn different machine learning and deep learning approaches in the financial context and gain experience applying the models to different financial data types and applications through multiple hands-on demonstrations and exercises using Python.
Level: 5
Credit Units: 5
Presentation Pattern: EVERY JAN

Topics

  • Understanding ML & DL
  • Fundamentals of classic ML models such as k-means, logistic regression, support vector machine (SVM)
  • Fundamentals of classic DL models such as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN)
  • Introduction to different finance data types (e.g. time series data, cross-sectional data, and texts)
  • Framework of ML & DL Application in Finance
  • Risk analysis and forecasts using predictive modelling
  • Stock market prediction using multilayer neural network architecture
  • Textual analysis adopted in annual reports, media news and white papers
  • Sentiment analysis and topic modelling in Python
  • Portfolio analysis by machine learning
  • Python programming for machine learning
  • Python programming for deep learning

Learning Outcome

  • Assess various ML & DL models
  • Appraise the importance and application scenarios of ML and DL frameworks
  • Design ML and DL models to tackle financial issues and analyse model performance
  • Construct and implement deep learning and machine learning models in different financial practices
  • Compose sentiment analysis on textual data
  • Develop the programming skills used to model ML and DL applications in finance
Part-time Programmes / CET Courses

Date and Duration

DayDateWeekTime
Thursday05/03/2026819:00 - 22:00
Thursday12/03/2026919:00 - 22:00
Thursday19/03/20261019:00 - 22:00
Thursday26/03/20261119:00 - 22:00
Thursday02/04/20261219:00 - 22:00
Thursday09/04/20261319:00 - 22:00


Target Audience

Entrepreneurs, mid-career bankers, executives and finance professionals.

 

Relevance of Course to employment/upskilling/reskilling

FIN525 Machine Learning, Deep Learning and Applications in Finance supports employment, upskilling, and reskilling by equipping learners with knowledge and hands-on skills to build and implement machine learning and deep learning models in finance using Python. The course develops practical skills directly applicable to roles such as data analyst, quantitative analyst, risk analyst, fintech specialist, and AI/ML practitioner in financial services. The course also supports upskilling and reskilling by bridging the gap between traditional finance knowledge and modern AI-driven techniques and providing industry-relevant competencies.


Admissions Pre-requisite

Refer to graduate CET admissions and also include Admission Eligibility Criteria for Graduate CET Modular Courses (Admission Eligibility Criteria for Graduate CET Modular Courses).

 

Schedule

TimeAgendaDeliverables
Session 1
  • 19:00 – 20:30 Understanding ML & DL
  • 20:30 – 20:45 Break
  • 20:45 - 22:00 Fundamentals of classic ML models such as k-means, logistic regression, support vector machine (SVM)
-
Session 2
  • 19:00 – 20:30 Fundamentals of classic DL models such as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN)
  • 20:30 – 20:45 Break
  • 20:45 - 22:00 Introduction to different finance data types (e.g. time series data, cross-sectional data, and texts)
-
Session 3
  • 19:00 – 20:30 Framework of ML & DL Application in Finance
  • 20:30 – 20:45 Break
  • 20:45 - 22:00 Risk analysis and forecasts using predictive modelling
-
Session 4
  • 19:00 – 20:30 Stock market prediction using multilayer neural network architecture
  • 20:30 – 20:45 Break
  • 20:45 - 22:00 Textual analysis adopted in annual reports, media news and white papers
-
Session 5
  • 19:00 – 20:30 Sentiment analysis and topic modelling in Python
  • 20:30 – 20:45 Break
  • 20:45 - 22:00 Portfolio analysis by machine learning
-
Session 6
  • 19:00 – 20:30 Python programming for machine learning
  • 20:30 – 20:45 Break
  • 20:45 - 21:30 Python programming for deep learning
  • 21:30 - 22:00 In-Class Assessment
In-Class Assessment

 

Assessments

Assignments, Others, PARTICIPATION

 

Trainer info

Dr Wang Zhiyuan is a lecturer at the Singapore University of Social Sciences, where he teaches the finance programme within the School of Business. Prior to this, he served as an assistant professor at the DigiPen Institute of Technology Singapore. He holds a B.Eng. and a Ph.D. from the National University of Singapore (NUS) and an M.Sc. from the Nanyang Technological University (NTU).

 

Course Completion requirements

Participants are required to achieve at least 75% attendance and pass any prescribed examinations/assessments or submit any course/project work (if any) under the course requirement.

  • Participants are required to complete all surveys and feedbacks related to the course
  • The course fees are reviewed annually and may be revised. The University reserves the right to adjust the course fees without prior notice.
  • Singapore University of Social Sciences reserves the right to amend and/or revise the above schedule without prior notice

 

Course Fees, payment and refund policy

 International Participants Singapore Citizens (below 40yrs), Permanent Residents Singapore Citizens (40yrs and above) SkillsFuture Mid - Career Enhanced Subsidy1Enhanced Training Support for SMEs2 (Singaporean and PRs)
Course Fees (A) $3,168.00 $2,640.00 $2,640.00 $2,640.00
SSG Grant (70%) (B)  $1,848.00 $1,848.00 $1,848.00
Nett Course fees (A) - (B) = (C) $3,168.00 $792.00 $792.00 $792.00
9% GST on Nett course fees (D) $285.12 $71.28 $71.28 $71.28
Total nett course fees payable including GST (C) + (D) $3,453.12 $863.28 $863.28 $863.28
Less additional funding if eligible under various schemes (F) $-   $-   $528.00 $528.00
Total nett course fees payable including GST, after additional funding form the various schemes (E) - (f) = (H) $3,453.12 $863.28 $335.28 $335.28
1 Mid-Career Enhanced Subsidy: Singaporeans aged 40 and above may enjoy subsidies up to 90% of the course fees.
2 Enhanced Training Support for SMEs: SME-sponsored employees (Singapore citizens and PRs) aged 21 and above may enjoy subsidies up to 90% of the course fees.

For the various payment mode, please refer here.
For the refund policy, please refer here. 

 

For clarification, please contact the SUSS Academy via the following:
Telephone: +65 6248 0263
Email: [email protected]