CS-4112 Introduction to Machine Learning

Instructor: Arbish Akram

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This course covers the theory and practice of AI algorithms that learn from data to make predictions, including supervised learning, unsupervised learning, and neural networks.

Text

1. Pattern Recognition and Machine Learning by Christopher M. Bishop

Grading

Schedule

Lecture	Topics	Reading Material	Tutorials	Quizzes	Assignments
1	Introduction
2-3	Curve Fitting	Bishop 1.1	Tutorial 1: Linear Regression
4-6	Linear Regression	Bishop 3.1.1		Quiz 1, Quiz 2	Assignment 1
7	Rules of Probability	Bishop 1.2
8	Statistics	Bishop 1.2.2		Quiz 3, Quiz 4
9	Loss Functions
10	Activation Functions			Quiz 5, Quiz 6
11-12	Neural Networks
13-14	Backpropagation and Vanishing Gradients
15-16	Gradient Descent and its Variations
17	Regularization
18	Convolutional Neural Network
19-20	Variations of CNN
21	K-Means Clustering
22	K-Nearest Neighbor
23	Decision Trees