CSE-411 Machine Learning

E-mail: atik@cse.green.edu.bd

🕾 Mob. +8801912961096

Room: A-510 Desk No. : 06

Class Routine – Summer 2025 Semester

Time Day	08:30 - 10:00	10:00 - 11:30	11:30 - 01:00	Break	01:30 - 03:00	03:00 - 04:30
Sat	CSE 412 221_D3 K-101	CSE 412 221_D3 K-101			CSE 411 221_D3 K-105
Sun	CSE 318 231_D3 K-101	CSE 318 231_D3 K-101			CSE 411 221_D3 K-105
Mon	CSE 412 221_D3 K-101	CSE 412 221_D3 K-101			CSE 411 221_D3 K-105	Capstone Project/Thesis Meeting
Tue	CSE 318 231_D3 K-101	CSE 318 231_D3 K-101
Wed

Topic Outline

Lecture	Selected Topic	Article	Problems
(1)	Introduction	Class Notes
(2-6)	Supervised Learning (Regression, Classification, Linear Regression, Logistic Regression, Importance of designing effective cost function, convex function, learning parameters and parameter optimization concepts)	Class Notes	Assignment 1
(7-10)	Bayesian Decision Theory (review of probability concepts, uncertainty modeling, likelihood, posterior probability, naive decision rules, sensitivity and specificity)	Class Notes
(11-12)	Parametric and non-parametric Methods for density estimation	Class Notes	Quiz 1
(13-14)	Unsupervised Learning (Association rule, KMeans Clustering, etc.)	Class Notes
	Midterm Exam
(15-15)	Perceptron learning (basic architecture and limitations)	Class Notes	Call for a Group Project
(16-19)	Multilayer Perceptrons (importance of non-linearity, understanding artificial neural network architecture, cost function, understanding multivariate calculus and its role in Neural networks, Stochastic Gradient Descent optimization, hyperparameter tuning)	Class Notes
(20-21)	Introduction to Graphical Models	Class Notes	Quiz 2
(22-25)	Time series modeling/online learning (Markov model, Hidden Markov Models, and their applications, Bayesian Networks)	Class Notes
(26-28)	Reinforcement Learning (Markov decision processes and Q-learning)	Class Notes
(29-30)	Design and Analysis of Machine Learning Experiments	Class Notes
	Final Exam