ML1- Introduction to Machine Learning
For signups and further details, please contact
Ms. Irene Ong Hwei Nee.
- The course follows a MOOC-style approach with the following components:
- Lectures: Each lecture, up to 2 hours, is delivered via pre-recorded video.
- Programming Labs: 8 automated labs (integrated with Lectures 1–8) are hosted on Coursemology.
- The format of the Final Contest will be Live Kaggle Competition + Short MCQ Quiz.
- Students are expected to follow the given Learning Flow at their own pace to prepare themselves for the Final Contest.
- L0 – Tools Bootcamp (Optional, 1 hour)
- L1 – Introduction
- L2 – k-Nearest Neighbor
- L3 – Decision Trees
- L4 – Ensembles
- L5 – 1D Linear Models
- L6 – k-Means Clustering
- L7 – Model Evaluation
- L8 – Data Representation and Feature Engineering
- L9 – Course Review