Machine Learning
Instructor
Dr. Janibul Bashir
Course Overview
In this course, our aim is
(a) to familiarize with/develop the understanding of fundamental concepts of Machine Learning (ML)
(b) To develop the understanding of working of a variety of ML algorithms (both supervised as well as unsupervised)
(c) To learn to apply ML algorithms to real world data/problems
(d) To update with some of the latest advances in the field
Course Code
PHD 14
Lectures
Mon, Thur (L1)
Reference Material for the course:
B1- Probabilistic Machine Learning: An Introduction. Kevin Murphy.
B2- Pattern Recognition and Machine Learning. Christopher Bishop. First Edition, Springer, 2006.
B3- Pattern Classification. Richard Duda, Peter Hart and David Stock. Second Edition, Wiley-Interscience, 2000.
B4- Machine Learning. Tom Mitchell. First Edition, McGraw-Hill, 1997.
B5 - Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron
B6 - Notes from CS229
Video Lectures: 1. Machine Learning using Python by Dr. Janibul Bashirs [Videos]
2. Stanford CS229: Machine Learning by Prof. Andrew NG [Videos]
Announcements:
Classes will be held in High Performance Computing Seminar Room from 21-08-2023.