Embedded Files
Instructor
Instructor
Dr. Janibul Bashir
Course Overview
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
Course Code
ITT352
Venue
Venue
PF3
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 by Dr. Janibul Bashirs [Videos]
2. MIT's 6.036 course: Introduction to Machine Learning. [Course Website]
Announcements:
Classes will be held in PF3
Python will be the default language that will be used to solve the assignments.
You are free to discuss the assignment problems with other students in the class. But all your code should be produced independently without looking at/referring to anyone else's code.
Honor Code: Any cases of copying will be awarded a zero on the assignment and a penalty of -10. More severe penalties may follow.
Course Content
Course Content
Introduction to Machine Learning and Linear Classifiers
Introduction to Machine Learning and Linear Classifiers
(11-02-2026) -- [Lecture 1] -- Introduction to Machine learning - [Video Lecture] [B5 Chapter 1]
(11-02-2026) -- [Lecture 2] -- Supervised Learning, Hypothesis Function and Linear Classifier-- [Video Lecture] [B6 Part 1] [MIT Course Lec 1]
(12-02-2026) -- [Lecture 3] -- Linear Classifier, 0-1 Loss, Naive Algorithm - [Video Lecture] [MIT Course Lec 2]
(12-02-2026) -- [Lecture 4] -- Perceptron Algorithm - [Video Lecture] [MIT Course Lec 2]
(16-02-2026) -- [Lecture 5] -- Probabilistic Modelling | Log Likelihood | Maximum Likelihood Estimation - [Video Lecture] [MIT Course Lec 4]
(16-02-2026) -- [Lecture 6] -- Logistic Regression | Sigmoid Function | Gradients | Cross Entropy Loss - [Video Lecture] [MIT Course Lec 4]
(17-02-2026) -- [Lecture 7] -- Gradient Descent | Logistic Regression | Contour Plots | Cross Entropy Loss - [Video Lecture] [MIT Course Lec 4]
(18-02-2026) -- [Lecture 8] -- Multi Class Classification | One Vs All | Softmax Regression | One Hot Vector - [Video Lecture]
(18-02-2026) -- [Lecture 9] -- Multi class Loss | Cross Entropy Loss | Log Likelihood - [Video Lecture]
(23-02-2026) -- [Lecture 10] -- Classification Metrics | Recall | Precision | Confusion Matrix | Accuracy - [Video Lecture]
(23-02-2026) -- [Lecture 11] -- ROC Curve | Precision Recall Curve | Area Under Curve | AUC - [Video Lecture]
Linear Regression and Regularization
Linear Regression and Regularization
(25-02-2026) -- [Lecture 12] -- Linear Regression | Curve Fitting | Regression Problem | Gradient Descent – [Video Lecture] [MIT Course Lecture 5] [B6 Chapter 1] [B5 Chapter 4]
(25-02-2026) -- [Lecture 13] -- Gradient Descent | Linear Regression | Matrix Formulation | Mean Square Error -- [Video Lecture] [MIT Course Lec 5] [B6 Chapter 1.1] [B5 Chapter 4]
(26-02-2026) -- [Lecture 14] -- Normal Equations | Limitations | Polynomial Regression | Overfitting -- [Video Lecture] [B6 Chapter 1.2][B5 Chapter 4]
(05-03-2026) -- [Lecture 15] -- Generalization | Underfitting | Overfitting | Bias-Variance Tradeoff | Learning Curves -- [Video Lecture] [B6 Chapter 8][B5 Chapter 4]
(10-03-2026) -- [Lecture 16] -- Need of Regularization in Machine Learning -- [Video Lecture] [B6 Chapter 9][B5 Chapter 4]
(10-03-2026) -- [Lecture 17] -- Regularization: Ridge and Lasso, Early stopping, Cross-validation and SGD -- [Video Lecture] [B6 Chapter 9][B5 Chapter 4]
Support Vector Machines and Kernel Methods
Support Vector Machines and Kernel Methods
(12-03-2026) -- [Lecture 18] -- Support Vector Machines | Introduction | Functional Margin -- [Video Lecture] [Tutorial]
(12-03-2026) -- [Lecture 19] -- Support Vector Machines | Geometric Margin | Loss | Limitations of Hard Margin -- [Video Lecture]
(16-03-2026) -- [Lecture 20] -- Limitations of Hard SVM | Soft Support Vector Machines | Slack Variables | Hinge Loss -- [Video Lecture]
(17-03-2026) -- [Lecture 21] -- Kernels Methods | Features Maps -- [Video Lecture] [Python Example][Master Notes for CS229 Section 5.1 - 5.4]
(18-03-2026) -- [Lecture 22] -- Kernels Methods | Kernel Trick | Gram Matrix | Kernel Functions -- [Video Lecture]
Assignments
Assignments
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