Machine Learning
NUS SoC, 2020/2021, Semester I, Time and Venue: fully online via Zoom and YouTube. Officially from LumiNUS: Mondays, 16:00-18:00 and Thursdays, 11:00-12:00. -
This page is for a previous semester, and is kept on the web for archival purposes only. Go to the current webpage for the course.
This module introduces basic concepts and algorithms in machine learning and neural networks. The main reason for studying computational learning is to make better use of powerful computers to learn knowledge (or regularities) from the raw data. The ultimate objective is to build self-learning systems to relieve human from some of already-too-many programming tasks. At the end of the course, students are expected to be familiar with the theories and paradigms of computational learning, and capable of implementing basic learning systems.
We will be using the Coursemology Learning Management System for the administration of this course (the Coursemology course will be published in due time).
N.B. We will be teaching and using the Python programming language throughout this class and Jupyter Notebook via Google Colab. We will using Python 3.x, and largely the SciKitLearn and PyTorch libraries.
Class Structure
This class is a flipped class, a variant of a blended class. You’ll watch the first part of the video lecture before coming to tutorial, and then watch a subsequent video recorded lecture post-tutorial, to further reinforce the tutorial.
Important: Lecture slots are for e-learning, and for occasional assessments (in Weeks 07 and 13).
Do ensure that you have the lecture times free in your schedule to be available for scheduled exams. There will no be alternate exam arrangements.
For most of the Thursdays sessions, we will have a synchrnous help session where Min will field questions from students on questions, concerns and doubts you have on the lecture materials or other components of the course.
Tutorial Sessions
There will be tutorials for this class starting in Week 03. As the class is flipped, these sessions will be the primary means by which we touch base with you and get to know you personally. Please do attend these sessions via Zoom, as they will not be webcasted (although tutorial solutions will be distributed, you should come to the sessions to get the complete picture, and to be a part of the class).
These tutorial session timings still subject to change. Please see NUSMods for the most up-to-date details. As an enrolled student, you are entitled to one tutorial placement, and need to attend that slot even if not optimal for you. Nicely, all of the tutorials slots come before (Monday-Wednesday), the second class lecture slot on Thursdays.
Tutorial Sessions (tentative; as reported by LumiNUS ca. 17 Jun 2020):
- T01. Mondays, 13:00-14:00
- T02. Wednesdays, 17:00-18:00
- T03. Mondays, 15:00-16:00
- T04. Mondays, 14:00-15:00
- T05. Wednesdays, 11:00-12:00
- T06. Mondays, 12:00-13:00
- T07. Wednesdays, 16:00-17:00
- T08. Wednesdays, 10:00-11:00
- T09. Tuesdays, 16:00-17:00
- T10. Tuesdays, 17:00-18:00
- T11. Tuesdays, 12:00-13:00 (new as of 11 Aug 2020)
- T12. Tuesdays, 13:00-14:00 (new as of 11 Aug 2020)
Course Characteristics
Modular Credits: 4.
Prerequisites: (CS2010 or its equivalent) and (ST1232 or ST2131 or ST2132 or ST2334) and (MA1101R or MA1311 or MA1506) and (MA1102R or MA1505 or MA1521)
Translation: Linear algebra, calculus, probability and statistics and introductory computer programming.
Questions about prerequisities and waivers are handled centrally by the department. Please contact OOI Wei Tsang) to describe your case and seek any waivers regarding prerequisities.
Instructors
kanmy@comp.nus.edu.sg
AS6 05-12
Office hours are held (before and after class), but more commonly by appointment. Emails to me as a default are assumed to be public, and my replies and your anonymized email will likely be posted to Coursemology. Please let me know if you do not want the contents of your email posted; I will be happy to honor your requests.
Teaching Assistants
Graduate Teaching Assistants
Undergraduate Teaching Assistants
Workload
(3-1-0-3-3)
Translation:
3 lecture hours per week (flipped) 1 hour of tutorials 3 hours for projects, assignments, fieldwork, etc. per week 3 hours for preparatory work by a student per week