DS 675: Machine Learning
Spring 2024

Instructor: Usman Roshan
Office: GITC 4415
Office Hours:M,W,Th 1-3pm
Ph: 973-596-2872
Email: usman@njit.edu

TA: Ching-Hao Fan
Email: cf322@njit.edu

See course Canvas page for recorded lectures, quizzes, assignments, and exams

Textbooks:
Introduction to Machine Learning by Ethem Alpaydin (Not required but strongly recommended)
Learning with kernels by Scholkopf and Smola (Recommended)
Foundations of Machine Learning by Rostamizadeh, Talwalkar, and Mohri (Recommended)

Additional course material:

Topic	Date	Notes
Linear modeling		Linear models Least squares notes Least squares gradient descent algorithm Regularization Stochastic gradient descent pseudocode Stochastic gradient descent (original paper)
Kernels		Kernels More on kernels
Multiclass classification		Multiclass classification One-vs-all method
Logistic regression		Logistic regression
Empirical and regularized risk minimization		Empirical risk minimization Regularized risk minimization Regularization and overfitting
Support vector machine		Support vector machines
Decision trees and random forests		Decision trees, bagging, boosting, and stacking Decision trees (additional notes) Ensemble methods (additional notes)
Feature selection		Feature selection Feature selection (additional notes)
Dimensionality reduction		Dimensionality reduction
Clustering		Clustering
Maximum likelihood		Bayesian learning
Neural networks		Multilayer perceptrons Single hidden layer neural network Back propagation
Autoencoders		Generative models and networks Autoencoder