Dr. Janibul Bashir
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
Tue, Wed, Thur (IT Department)
Classes will be held in the seminar room of the IT Department from 02:00 - 03:00 PM
Python will be the default language that will be used to solve the assignments.
(06-04-2022) -- [Lecture 1] Introduction to Machine learning and Supervised Learning. [Notes for CS229]
(18-04-2022) -- [Lecture 5] Random Variable, PDF, PMF, CDF, Expected Value, Conditional Probability. [Notes for CS229 Section 1 and 2]
(19-04-2022) -- [Lecture 6] Joint Distribution, Multiple Random Variables, Multivariate Gaussian Distribution, Maximum Log Likelihood [Notes for CS229 Section 3, 4 and 5]
Gaussian Discriminant Analysis
Support Vector Machines
(16-05-2022) -- [Lecture 11] Introduction to Support Vector Machines [Master Notes for CS229 Section 6.1, 6.2]
(18-05-2022) -- [Lecture 13] SVM for non-separable datasets, L1 loss, L2 loss, Hinge Loss [Master Notes for CS229 Section 6.7]
Midterm Exam [23-05-2022 to 27-05-2022]
Deep Learning - Neural Networks
Homework: Build a cat vs non-cat model both using logistic regression and dense neural network and compare their accuracies.
(22-06-2022) -- [Lecture 21] CNN Architectures and TensorFlow Implementations: LeNet5, AlexNet [CNN Architrectures]
Homework: Develop a CNN that classifies the objects present in the MNIST Fashion Dataset.
Homework: Use any object detection algorithm to detect the different types of objects in an image.