CSE-411 Machine Learning

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Room: A-510 Desk No. : 06

Time Day	09:00 - 10:20	10:20 - 11:40	11:40 - 01:20	Break	01:30 - 02:50	02:50 - 04:10
Sat	CSE-435 212_D1 R: J-109	Research Time	CSE-436 212_D2 R: K-101
Sun	CSE-315 221_D10 R: A-605	Tutor Time	CSE-436 213_D1 R: A-501
Mon		Research Time			Weekly Academic Meeting
Tue	CSE-315 221_D10 R: A-605	Tutor Time			CSE 316 221_D20 R: K-109
Wed
	08:30 - 09:50	09:50 - 11:10	11:10 - 12:50	Break	02:00 - 03:20	03:20 - 04:40
Fri	CSE-435 212_D1 R: J-109	Tutor Time	CSE-436 212_D1 R: J-108

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