CMSC 422 - Introduction to Machine Learning


Welcome to CMSC 422. Machine Learning studies representations and algorithms that allow machines to improve their performance on a task from experience. This is a broad overview of existing methods for machine learning and an introduction to adaptive systems in general. Emphasis is given to practical aspects of machine learning and data mining.


Exam Dates:

  • Midterm: Tuesday, October 11th, in Lecture.
  • Final Exam: Monday, Dec. 19, 6:30pm - 8:30pm, Location: PHY 1410

Lectures (Tentative)

Week Starting Tuesday Thursday
08/29 Course Intro
Welcome to Machine Learning

Decision Trees

Reading: Chapter 1 of the text book
09/05 Nearest Neighbor Classification

Reading: Chapter 3 of the text book

Reading: Chapter 4 of the text book
09/12 Convergence Analysis of Perceptron

Linear Classifiers, Gradient Descent and Hinge Loss

Reading: Chapter 7 of the text book
09/19 Gradient Descent Part II

Reading: Chapter 7 of the text book
GD (part III)+ Probabilistic View of ML, Naive Bayes

Reading: Chapter 9 of the text book
09/26 Naive Bayes Classifier Logistic Regression

Reading: Part II of notes
10/03 Logistic Regression br>
Reading: Part II of notes
Multi-label Classification<

10/10 Midterm Neural Networks

Reading: Chapter 10 of the text book
10/17 Nonlinear Regression + Back Propagation Back Propagation

Reading: Chapter 10 of the text book
10/24 Multi Label Classification, Vanishing Gradients, Momentum method

Reading: Chapter 10 of the text book
More Deep Neural networks, CNN

10/31 Adversarial Robustness + Recurrent Neural Networks Unsupervised Learning + K-Means, PCA

11/07 PCA Analysis AutoEncoders

Reading: Chapter 11 of the text book
11/14 Kernels

Reading: Chapter 11 of the text book
Deep Learning frameworks / GANs

11/21 Gaussian Mixture Models

Reading: Chapter 7 of the text book
11/28 Expectation Maximization

Reading: Chapter 16 of the text book
12/05 Final Presentations I Final Presentations II


Instructor: Mohammad Nayeem Teli (nayeem at

Office: IRB 1128
Office Hours: Tuesdays, 4:45 PM - 5:30 PM, IRB 1128

Teaching Assistants

Name Email
John D Kanu jdkanu at
Tin Trung Nguyen tintn at

Office Hours

Instructor: Tuesdays, 4:45 PM - 5:30 PM, IRB 1128

Teaching Assistants

Office hours (AVW 4140 )
Tuesday Tin: 1:00 - 3:00 PM
Wednesday John: 3:00 - 5:00 PM
Thursday Tin: 1:00 - 3:00 PM
Friday John: 3:00 - 5:00 PM

Please note that a TA may need to leave 5 minutes before the end of the hour in order to go to his/her class. Please be understanding of their schedules.

Class Resources

Online Course Tools
  • ELMS - This is where you access homeworks/ assignments, submit them and go to see grades on assignments and to get your class account information.
  • Piazza - This is where you ask questions and discuss.

Assignments (On ELMS)

Homework Due Date*
Homework 1: Warm Up Tuesday September 06, 2022
Homework 2: Decision Trees Tuesday September 13, 2022
Homework 3: High Dimensional Space Thursday September 22, 2022
Homework 4: Linear Models and Perceptron Thursday September 29, 2022
Homework 5: Multiclass Classification Thursday October 20, 2022
Project 1: Classification Monday October 31, 2022
Programming Assignment 1: Multiclass and Linear Models Tuesday November 15, 2022
Homework 6: Principal Component Analysis Friday November 18, 2022
Project 3: Principal Component Analysis Saturday December 03, 2022
Final Project Presentations In class, December 6 and 8, 2022
Final Project Presentations (pdf) on ELMS Thursday December 8, 2022
Final Project Report Thursday December 15, 2022

*All homeworks/assignments are due at 11:59 PM on the due date.