Schedule
We note that Machine Learning is a subject with a lot of very good expertise and tutorials out there. It is best to tap on these resources, as they have good production quality and are more condensed, possibly saving you time. However, we still think in-class lecture is helpful to build better connection with the materials for certain topics.
This class will be flipped; i.e., you will be asked to watch videos on YouTube explaining the concepts on your own first (the pre videos), and then after the appropriate tutorial session where staff will guide you through the pertinent exercises and reinforcement activities. Post-tutorial, you will be expected to complete the second half of the videos (the post videos) and complete a set of mastery exercises in Coursemology. Note that the dates in the date column below are indexed for Mondays (the day of the first class lecture according to the registrar).
To be clear, the dates on this website are just for easy reference, but the authoritative dates will always be in Coursemology. Take note of any conflicting deadlines and let us know.
| Date | Description | Deadlines |
|---|---|---|
| NUS Week 01 Mon, 10 Aug | Administrivia and Paradigms of Learning | · Thu, 13 Aug 12:00-14:00: In-class session via Zoom |
| Week 02 17 Aug | Concept Learning | · Fri, 21 Aug 23:59: Form subteams of Size 3 |
| Week 03 24 Aug | Naïve Bayes and k-Nearest Neighbors T01: Paradigms of Learning | |
| Week 04 31 Aug | Linear Classifiers and Logistic Regression T02: Concept Learning, Naïve Bayes and k-Nearest Neighbors | · Fri, 4 Sep 23:59: Project Proposals Due |
| Week 05 7 Sep | Bias and Variance and Overfitting T03: Linear Classifiers and Logistic Regression | · Fri, 11 Sep 23:59: Peer Grading of Project Proposals Due |
| Week 06 14 Sep | Regularization and Validation T04: Bias, Variance and Overfitting | |
| Week Recess 21 Sep | ||
| Week 07 28 Sep | Reinforcement Learning and Exam 1 | · Mon, 28 Sep 16:00-18:00: In-class Exam 1 · 28 Sep-2 Oct: Interim Project Consultations with Staff |
| Week 08 5 Oct | Neural Networks T05: Regularization and Validation | · 5-9 Oct, Interim Project Consultations with Staff |
| Week 09 12 Oct | Deep Learning T06: Reinforcement Learning | · STePS teams: 12-16 Oct, 2nd Project Consultations with Staff |
| Week 10 19 Oct | Decision Trees and Ensembles T07: Neural Networks | · STePS teams: 19-23 Oct, 2nd Project Consultations with Staff · Non-STePS teams: Fri, 23 Oct 23:59: Project Posters and Videos Due |
| Week 11 26 Oct | Unsupervised ML: k Means and Expectation Maximization T08: Deep Learning | · Non-STePS teams: Poster Presentations to Staff · Non-STePS teams: Fri, 30 Oct 23:59: Peer grading of the Non-STePS Project Posters and Videos Due |
| Week 12 2 Nov | Machine Learning Ethics T09: Decision Trees and Ensembles | · STePS teams: Fri, 6 Nov 23:59: Project Posters and Videos Due |
| Week 13 9 Nov | Revision and Exam 2 T10: Unsupervised ML | · Mon, 9 Nov 16:00-18:00: In-class Exam 2 (Updated) · STePS teams: Wed, 11 Nov 15:00-20:00: (Updated) Participation on afternoon of 17th STePS · (Changed) · STePS teams: Fri, 13 Nov 23:59: Peer grading of peer STePS Project Posters and Videos Due All teams: Fri, 13 Nov 23:59: Project Reports Due |
| Exam Week TBA | No Examinations. 100% Continuous Assessment. |