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: Thursday, October 12th, in Lecture.
  • Final Exam: Wednesday, Dec. 13, 8:00 am - 10:00 am, Location: EGR 1202

Lectures (Tentative)

Week Starting Tuesday Thursday
08/28 Course Intro
Welcome to Machine Learning

Decision Trees

Reading: Chapter 1 of the text book
09/04 Ensemble learning

K-Nearest Neighbors

Reading: Chapter 3 of the text book
09/11 K-NN wrap up / Perceptron

Reading: Chapter 4 of the text book
Convex Review
09/18 Convergence Analysis of Perceptron

Reading: Chapter 4 of the text book
Linear Classifiers and loss functions

Reading: Chapter 7 of the text book
09/25 Gradient Descent

Reading: Chapter 7 of the text book
GD (part II) and subgradients

10/02 Naive Bayes Classifier

Reading: Chapter 9 of the text book
Logistic Regression

Reading: Part II of notes
10/09 Logistic Regression Contd. Midterm
10/16 Binary to Multi-label Classification (OVR & AVA)/
Logistic Regression Contd. /
Multi-class (softmax)
Neural Networks

Reading: Chapter 10 of the text book
10/23 Neural Networks Contd.

Reading: Chapter 10 of the text book
Nonlinear Regression /
Forward Propagation

Reading: Chapter 10 of the text book
10/30 Back Propagation

Reading: Chapter 10 of the text book
Multi Label Classificationn

Reading: Chapter 10 of the text book
11/06 Vanishing Gradients, Momentum method
Reading: Chapter 10 of the text book
Convolution Neural Network (CNN)
11/13 Convolution Neural Network (CNN) Contd. Unsupervised Learning - K-Means
11/20 Thanksgiving Break
11/27 Principal Components Analysis (PCA) AutoEncoders & Kernels (SVM)

Reading: Chapter 11 of the text book


Instructor: Mohammad Nayeem Teli (nayeem at

Office: IRB 2224
Office Hours:

Teaching Assistants

Name Email (at
Yijun Liang yliang17
Isabelle Armene Rathbun irathbun
Yan Wen ywen1
Wenshan Wu wwu009

Office Hours

Instructor: Mon 2:00 - 4:00 PM

Teaching Assistants

Office hours (AVW 4140 )
Monday Yijun: 9:00 - 11:00 AM,
Wenshan: 1:00 - 3:00 PM
Tuesday Yijun: 9:00 - 11:00 AM,
Isabelle: 4:00 - 5:00 PM
Wednesday Yan: 10:00 AM - 12:00 PM
Isabelle: 3:00 - 5:00 PM
Thursday Wenshan: 12:15 - 2:15 PM
Isabelle: 4:00 - 5:00 PM
Friday Yan: 10:00 - 12:00 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.
  • Gradescope - This is where your projects are graded and you submit regrade requests

Assignments (On ELMS)

Homework Due Date*
Homework 1: Warm Up Tuesday September 05, 2023
Homework 2: Decision Trees Thursday September 14, 2023
Homework 3: High Dimensional Space Tuesday September 19, 2023
Homework 4: Linear Models and Perceptron Monday September 25, 2023
Project 1: Classification Saturday September 30, 2023

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