School of Computing

Machine Learning

NUS SoC, 2016/2017, Semester I
Lecture Theatre 19 (LT 19) / Tuesdays 14:00-16:00

Last Updated: November 16 - Final assessment venue updated to SR1.
September 10 - Reordered Ensembles and DTs to Week 9.
September 2 - Renumbered Tutorials.
July 23 - Added review and deleted reinforcement learning. Added preliminary flipped sessions.
July 4 - First port of syllabus.

Get the CS 3244 calendar as an link for use in calendaring applications, or see the calendar as a web page (never miss a class again, yeah right). Links to lecture notes and tutorials will start to function around the time the respective subject is covered in class.


New 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.

As such, portions of this class will be flipped; i.e., you will be asked to watch such videos explaining the concepts on your own first, and then come to class for a class-wide recitation, in which Min will guide you through some exercises. Flipped lessons are marked with flipped, but these are preliminary and are likely to change.

Week 1 (15 Aug)Administrivia and Supervised Learning
Week 2 (22 Aug)flipped Reviews: Probability, Statistics, and Linear Algebra
  • webcast Attend or watch and follow our iPython / Jupyter Notebook Tutorial
Week 3 (29 Aug)Feasibility of Learning and Linear Regression
  • Homework #1 released
  • Tutorial #1
Week 4 (5 Sep)flipped Logistic Regression and Gradient Descent
  • Tutorial #2
Week 5 (12 Sep)Support Vector Machines and Kernelization
  • Tutorial #3
Week 6 (19 Sep)Midterm and Industry Talk
  • Midterm (20% of final grade)
  • Tutorial #4
  • Homework #1 due
  • Homework #2 released
Recess Week
Week 7 (3 Oct) Bias and Variance and Overfitting
  • Tutorial #5
Week 8 (10 Oct)flipped VC Analysis, Regularization and Validation
  • Tutorial #6
  • Homework #2 due
  • Homework #3 (Mini ML project) released
Week 9 (17 Oct)Decision Trees and Ensemble Methods
  • Tutorial #7
Week 10 (24 Oct)Unsupervised Learning
  • Tutorial #8
Week 11 (31 Oct)flipped Neural Networks
  • Tutorial #9
Week 12 (7 Nov)Deep Learning Architectures and Revision
  • Tutorial #10
  • Homework #3 due
Week 13 (15 Nov)Three Learning Principles and Ethics
Reading Week
Final Assessment: 25 Nov (Sat), 9:00-11:00. Venue: Updated Seminar Room 1 (SR1; COM1 #02-06)