Synopsis
Topics
- Data preprocessing for Machine Learning
- Designing Machine Learning Workflows
- Supervised vs Unsupervised Learning
- Customer Segmentation
- Market Basket Analysis
- Click-through Rate (CTR) Prediction
- Churn Prediction
- Customer Lifetime value (CLV)
- Content-Based Recommendations
- Collaborative Filtering
- Machine Learning for Time Series
- Use Cases and Decision-Making
Learning Outcome
- Prepare data for machine learning models
- Design machine learning workflows
- Predict business output variables using machine learning methods
- Formulate appropriate machine learning models in business
- Evaluate the performance of machine learning models
- Improve business decision making via machine learning applications
Who Should Attend
Managers and executive involved in data-driven decision making
Relevance of Course to employment/upskilling/reskilling
This course effectively addresses skill gaps through comprehensive instruction in machine learning techniques for practical business applications, such as customer segmentation, CTR prediction, churn prediction, CLV prediction, recommendation engines, and time series data analysis. Geared towards business professionals seeking Artificial intelligence (AI) and machine learning-related job opportunities, it prepares learners for data-driven decision-making roles. Its industry relevance lies in the imparted in-demand machine learning skills, empowering businesses to leverage data for competitiveness, innovation, and enhanced customer experiences, making it a vital asset for professionals navigating the data-driven landscape of modern business.
Admissions pre-requisite
- An undergraduate degree or an equivalent qualification from a recognised institution
- Specific courses may have additional requirements or pre-requisite (e.g. counselling courses). For more information, you may contact the Head of Programme regarding additional course requirements or pre-requisite
Subject to your eligibility and the approval of the Head of Programme, credits earned (up to a cap of 30 credit units) from the completion of Graduate CET Modular courses from the suite of Graduate Programmes may be recognised when admitted to the relevant Graduate Programmes.
Schedule
| Time | Agenda |
|---|---|
| Week 1 | |
| 19:00 | Introduction & Course Overview |
| 19:30 | Designing Machine Learning Workflows |
| 20:30 | Break |
| 20:45 | Supervised vs Unsupervised Learning, Conclusion & Q&A |
| Week 2 | |
| 19:00 | Data preprocessing for Machine Learning |
| 20:30 | Break |
| 20:45 | Customer Segmentation, Market Basket Analysis, Conclusion & Q&A |
| Week 3 | |
| 19:00 | Click-through Rate (CTR) Prediction, Customer Lifetime value (CLV |
| 20:30 | Break |
| 20:45 | Churn Prediction, Conclusion & Q&A |
| Week 4 | |
| 19:00 | Content-Based Recommendations (Part 1) |
| 20:30 | Break |
| 20:45 | Content-Based Recommendations (Part 2), Conclusion & Q&A |
| Week 5 | |
| 19:00 | Collaborative Filtering (Part 1) |
| 20:30 | Break |
| 20:45 | Collaborative Filtering (Part 2), Conclusion & Q&A |
| Week 6 | |
| 19:00 | Machine Learning for Time Series |
| 20:30 | Break |
| 20:45 | Use Cases and Decision-Making |
| 21:30 | Feedback & Revision, In-class Assessment, Conclusion & Q&A |
Assessments
The overall course grade is determined by
- Others, Assignments
- Quiz, Case study
Trainer info
Dr Ren Jing (Ph.D., Singapore Management University) is a Senior Lecturer with the School of Business at SUSS. Dr Ren’s research spans interdisciplinary areas including artificial intelligence, recommendation systems, user-generated content analysis, and fintech. She teaches both theoretical and practical modules on AI across undergraduate and graduate programmes, covering domains such as Business Analytics, Finance, Analytics & Visualisation, and AI for Business.
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 Subsidy1 | Enhanced 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 |
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]