Note: This website is for Sem I, 2017/2018. The current version of this course follows a different syllabus and with a different lecturer, Dr Bryan Low. If you have questions about the current class, please contact him.
November 16 - Updated final assessment venue.
July 23 - Updated Tutorial 4 timing, fixed syllabus.
July 4 - First update.
Module Description, Aims and Objectives:
Module Description, Aims and
Objectives: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
Starting this semester, I will also be maintaining a
Facebook page (accessible from the FB
link on the top menu) for this course across cohorts.
Current students and alumni are welcome to contribute news
and items of potential interest to the page (i.e., ML
news, job openings specific to ML).
N.B. We will be teaching and using the Python programming
language throughout this class. We will using Python 3.4.x
- 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)
Office: AS6 05-12 (x61885).
- Mr Karim Magdi Abdelfattah Ibrahim
- Mr Jay Nandy
- Mr Ryutaro Oikawa
- Mr Animesh Prasad
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
IVLE. Please let me know if you do
not want the contents of your email posted;
I will be happy to honor your requests.
- Workload: (2-1-0-3-3) Translation:
2 lecture hours per week
1 hour of tutorials or labs per week
3 hours for projects, assignments, fieldwork, etc. per week
3 hours for preparatory work by a student per week
Yaser S. Abu-Mostafa, Malik Magdon-Ismail,
Hsuan-Tien Lin. (2012) Learning from data :
a short course.
LINC for book ]
[ Book website (includes supplemental material and forum) ]
- Christopher M. Bishop (2007) Pattern Recognition and Machine Learning.
LINC for book ]
- Hal III Daumé (2015) A Course on Machine Learning. Draft.
[ Book Website - entire digital copy available. ]
- Tutorials: Note: There will be
ten tutorial sessions. Tutorials start 31 Aug 2017.
Half will be live in the tutorial classroom, the
others may be recorded; class policy has yet to be
finalized for this.
- Tutorial Group 4: Thursdays, (09:00-10:00; SR9 COM1 #02-09)
- Tutorial Group 1: Thursdays, (12:00-13:00; SR9 COM1 #02-09)
- Tutorial Group 2: Thursdays, (13:00-14:00; SR9 COM1 #02-09)
- Tutorial Group 3: Thursdays, (14:00-15:00; SR9 COM1 #02-09)
- Tutorial Group 5: Fridays, (12:00-13:00; SR6 COM1 #02-03)
- Tutorial Group 6: Fridays, (13:00-14:00; SR6 COM1 #02-03)
- Final Assessment: 25 Nov 9:00-11:00. (Yes, we know -- we are going to ruin your Friday night and Saturday morning)
Venue: Seminar Room 1 (SR1;COM1 #02-06)
Note to NUS-external visitors: Welcome! If you're a fellow
ML course instructor looking for lecture material, you can see
the Syllabus menu item on the nav bar for a preview. Please contact
me if you'd like to use any of my material. Thanks!