Machine Learning
NUS SoC, 2021/2022, Semester I, Hybrid: Physically Mondays, 16:00-18:00 (i3 Auditorium) and Thursdays, 11:00-12:00 (LT15); Virtually on Zoom via LumiNUS Conferencing.
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.
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 synchronous hybrid class, a variant of a blended class. You’ll need to be synchronously present in the lecture (either in the lecture theatre or online in Zoom) to participate for class participation marks.
Important: Do ensure that you have the lecture times free in your schedule, as the class is synchronous. There will no be alternate exam arrangements.
For most of the Thursdays sessions, we will have a synchronous help session where Min and Brian 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 hybird, these sessions will be the complementary 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:
- 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
- T12. Tuesdays, 13:00-14:00
Course Characteristics
Modular Credits: 4.
Prerequisites: (CS2040 or its equivalent) and (EE2012A or ST2131 or ST2334) and (MA1101R or MA1311 or MA1508E or MA1512) and (MA1102R or MA1505 or MA1521)
Translation: Data Structures and Algorithms, Probability and Statistics, Linear Algebra and Calculus.
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
Teaching Assistants
Graduate Teaching Assistants
Undergraduate Teaching Assistants
Workload
(2-1-0-3-4)
Translation:
2 lecture hours per week
1 hour of tutorials
3 hours for projects, assignments, fieldwork, etc. per week
4 hours for preparatory work by a student per week