CMSC 422 - Introduction to Machine Learning



Class:

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.

Schedule

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) PCA Contd.
Intro to AutoEncoders

Reading: Chapter 11 of the text book
12/04 AutoEncoders & Kernels (SVM)

Reading: Chapter 11 of the text book 		Support Vector Machines (SVM)

Reading: Chapter 11 of the text book

Staff

Instructor: Mohammad Nayeem Teli (nayeem at cs.umd.edu)

Office: IRB 2224
Office Hours:


Teaching Assistants


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


Office Hours

Instructor: Mon 2:00 - 4:00 PM

Teaching Assistants

Day
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.