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

Tue, Wed, Thur (IT Department)

Reference Material for the course:

  1. B1- Probabilistic Machine Learning: An Introduction. Kevin Murphy.

  2. B2- Pattern Recognition and Machine Learning. Christopher Bishop. First Edition, Springer, 2006.

  3. B3- Pattern Classification. Richard Duda, Peter Hart and David Stock. Second Edition, Wiley-Interscience, 2000.

  4. B4- Machine Learning. Tom Mitchell. First Edition, McGraw-Hill, 1997.

  5. B5 - Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron

  6. B6 - Notes from CS229

Video Lectures: 1. Machine Learning using Python by Dr. Janibul Bashir [Videos]

2. Stanford CS229: Machine Learning by Prof. Andrew NG [Videos]

Announcements:

  1. Classes will be in virtual mode with offline discussions.

Course Content

Supervised Learning

(12-10-2022) -- [Lecture 1] -- Introduction to Machine learning - [Video Lecture] [B5 Chapter 1]

(14-10-2022) -- [Lecture 2] -- Supervised Learning, Hypothesis Functions, Loss Functions (Zero-One loss and Mean Square Loss) -- [Video Lecture] [B6 Part 1]

(19-10-2022) -- [Lecture 3] -- Linear Regression, Hypothesis Function, Matrix Format Dataset -- [Video Lecture] [B6 Chapter 1][B5 Chapter 4]

(21-10-2022) -- [Lecture 4] -- Gradient Descent -- [Video Lecture] [B6 Chapter 1.1][B5 Chapter 4]

(25-10-2022) -- [Lecture 5] -- Batch GD, Stochastic GD, Mini-Batch GD -- [Video Lecture] [B6 Chapter 1.1 and 1.4][B5 Chapter 4]

(27-10-2022) -- [Lecture 6] -- Normal Equations, Non-linear Hypothesis, Polynomial Regression -- [Video Lecture] [B6 Chapter 1.2][B5 Chapter 4]

(31-10-2022) -- [Lecture 7] -- Generalization, Underfitting, Overfitting, Bias-Variance Tradeoff, Learning Curves -- [Video Lecture] [B6 Chapter 8][B5 Chapter 4]

(01-11-2022) -- [Lecture 8] -- Regularization, Ridge and Lasso, Early stopping, Cross-validation -- [Video Lecture] [B6 Chapter 9][B5 Chapter 4]

Probabilistic Modelling

(02-11-2022) -- [Lecture 9] -- Introduction to probability and its relation to Machine Learning - [B1 Section 2.1]

(15-11-2022) -- [Lecture 10] -- Random variables and probability distributions - [B1 Section 2.2]

(16-11-2022) -- [Lecture 11] -- Bayesian Inference and Bayes Theorem - [B1 Section 2.3]

(17-11-2022) -- [Lecture 12] -- Bernoulli Distribution and Binary Logistic Regression using Bernoulli Distribution - [B1 Section 2.4]

(21-11-2022) -- [Lecture 13] -- Categorical Distribution and Multiclass logistic regression using categorical distribution - [B1 Section 2.5]

(23-11-2022) -- [Lecture 14] -- Probabilistic Modelling: Maximum likelihood estimation for parameter optimization - [B1 Section 4.1, 4.2]

Python Tutorials with shared code

(13-10-2022) -- [Tutorial 1] Python Basics - [Video Lecture] [Code]

(15-10-2022) -- [Tutorial 2] Numpy Library - Working with matrices and arrays - [Video Lecture] [Code]

(16-10-2022) -- [Tutorial 3] Pandas and Scikit-Learn Library - [Video Lecture] [Code]

(17-10-2022) -- [Tutorial 4] Matplotlib Library - Plotting the data - [Video Lecture] [Code]