This course aims to teach the basics of classical machine learning through hands on experiments in prediction and classification. This is the main component in a modern data science pipeline
- Graduate Certificate in Data Science and Artificial Intelligence
- Graduate Certificate in Artificial Intelligence and AI Plus
- FlexiMasters in Data Science and Artificial Intelligence
- FlexiMasters in Artificial Intelligence and AI Plus
At the end of the course, learners are able to:
1. build and train models on labelled datasets;
2. check model accuracy, and finally;
3. predict values or classes of target variables using those models.
1. Introduction to basics of Machine Learning
2. Linear Models for Predicting Numeric Variables
3. Tree Models for Predicting Categorical Variables
4. Concept of Bias and Variance in Model Fitting.
Python codes relevant to this module will be provided and will be used for hands-on demonstrations during the interaction session.
For learner who wish to acquire the knowledge and skills to boost their career prospects, become savvier in technology applications as well as better equipped for the fast paced advancements expected ahead.
Standard Course Fee: S$1,944
SSG Funding Support
Course fee payable after SSG funding, if eligible under various schemes
Fee BEFORE funding & GST
Fee AFTER funding & 8% GST
Singapore Citizens (SCs) and Permanent Residents (PRs) (Up to 70% funding)
Enhanced Training Support for SMEs (ETSS)
SCs aged ≥ 40 years old
• NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click here for more information.
|COURSE TITLE||ACADEMIC UNIT|
|CET787 Foundations of Computation Thinking and Programming||1|
|CET788 AI 1:AI Foundation||1|
|CET789 AI 2: Reinforcement Learning||1|
|CET790 AI 3: Computational Game Theory||1|
|CET791 ML1: Supervised learning: Bayesian decision theory and classifiers||1|
|CET792 ML2: Supervised learning: Non-probabilistic classifiers||1|
|CET793 ML3: Unsupervised learning||1|
|CET794 AI Ethics 1: Foundations of AI Ethics||1|
|CET795 AI Ethics 2: AI Ethic Standardization||1|
|CET802 DS1: Descriptive Analytics ||1|
|CET804 DS3: Pattern Recognition||1|
|CET805 AI Ethics 3: Ethics in Data Processing||1|
|CET806 Application 1: Introduction to Affective AI||1|
|CET807 Application 2: Introduction to Computer Vision||1|
|CET808 Application 3: Introduction to Cloud AI||1|
Listed courses are:
- Credit-bearing and stackable to Graduate Certificate in Data Science and Artificial Intelligence (total 9AUs), and FlexiMasters in Data Science and Artificial Intelligence (total 15AUs).
- SSG funded and SkillsFuture Credit approved.