Machine Learning (EC 332) - Department of Computer Science

Course Title: Machine Learning
Course Code: EC 332
Credits: 3
Instructor: Dr. Nazar Khan
Semester: Fall, 2024
Location: FCIT, Allama Iqbal Campus, Room 10
Class Times: Mondays and Wednesdays, 8:45 AM - 10:15 AM
Join Google Classroom 		Textbook:
Deep Learning: Foundations and Concepts (Bishop)
by Chris Bishop and Hugh Bishop.
Springer Nature
2024
dlfaccover 		Reference Books:
  1. Machine Learning: A Probabilistic Perspective (Murphy), Kevin P. Murphy, MIT Press, 2022.
  2. Reinforcement Learning: An Introduction (Sutton), 2nd Edition, Richard S. Sutton and Andrew G. Barto, MIT Press, 2018.
  3. Data Mining and Machine Learning: Fundamental Concepts and Algorithms (Zaki), 2nd Edition, Mohammed J. Zaki, Wagner Meira, Jr., Cambridge University Press, 2020.

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Welcome to the Machine Learning course page! Machine learning is a field of AI that focuses on the development of algorithms that can learn from and make predictions on data. In this course, you will study both the theoretical underpinnings and practical applications of machine learning techniques. This course covers the theory and practice of supervised learning, unsupervised learning, neural networks, deep learning, and reinforcement learning.

Lectures

Lecture No. Topics Covered Reading Material Quiz/Assign./H.W./Project
1 Introduction to Machine Learning Bishop 1.1-1.2 Tutorial 1: Linear Regression
2 Brief History of Machine Learning Bishop 1.3
3 Rules of Probability Bishop 2.1
4 Probability Densities Bishop 2.2 Quiz 1, Solution
5 Gaussian Distribution Bishop 2.3, 3.2 Tutorial 2: Gaussian Density Estimation
6 Discrete Random Variables Bishop 3.1 Assignment 1, Quiz 2, Solution
7 Linear Regression Bishop 4.1
8 Logistic Regression Bishop 5.4.3, 5.4.4 Quiz 3, Solution
9 Neural Networks Bishop 6.3
10 Loss Functions for Machine Learning Bishop 6.4
11 Gradient Descent Bishop Ch 7 Quiz 4, Solution
12 Automatic Differentiation Bishop 8.2
13 Regularization Bishop Ch 9 Assignment 2, Tutorial 3: Neural Network using PyTorch
14 Boosting Murphy 18.5.3
15 Non-parametric Density Estimation Bishop 3.5
16 K-Means Clustering Bishop 15.1
17 Mid-term Exam
18
19 DBSCAN Zaki 15.1 Project 1
20 Principal Component Analysis Bishop 16.1
21 Gaussian Mixture Models Bishop 15.2 Assignment 3
22 Expectation-Maximization (EM) Algorithm Bishop 15.3 Quiz 4
23 EM as a Variational Algorithm Bishop 15.4
24 Generative Adversarial Networks Bishop Ch 17
25 Reinforcement Learning Sutton Ch 1
26 Bandit Problem Sutton Ch 2
27 Markov Decision Processes Sutton Ch 3 Quiz 5
28 Dynamic Programming Sutton Ch 4 Assignment 4
29 Monte Carlo Methods Sutton Ch 5
30 Temporal-Difference Learning Sutton Ch 6
31 Transformers Bishop 12.1 Quiz 6
32 Transformer Language Models Bishop 12.2, 12.3
Final-term Exam

Grading

Assignments Project Quizzes Midterm Exam Final Exam
Weight 12% 8% 5% 35% 40%