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 23rd, in Lecture.
  • Final Exam: Saturday, Dec. 20, 1:30 PM - 3:30 PM, Location: TBA

Lectures (Tentative)


Week Starting Tuesday Thursday
09/01 Course Intro
Welcome to Machine Learning

 Decision Trees

Reading: Chapter 1 of the text book
09/08 Decision Trees Contd. / Ensemble learning

 K-Nearest Neighbors

Reading: Chapter 3 of the text book
09/15 Perceptron
Reading: Chapter 4 of the text book
Convex Review 		Convergence Analysis of Perceptron

Reading: Chapter 4 of the text book
Convex Review
09/22 Linear Classifiers and loss functions

Reading: Chapter 7 of the text book 		Gradient Descent

Reading: Chapter 7 of the text book
09/29 Naive Bayes Classifier

Reading: Chapter 9 of the text book		 Logistic Regression

Reading: Part II of notes
10/06 Binary to Multi-label Classification
(OVR & AVA)
Multi-class (softmax) 		Neural Network 1

Reading: Chapter 10 of the text book
10/13 Fall break (No class) Neural Network II

Reading: Chapter 10 of the text book
10/20 Back Propagation

Reading: Chapter 10 of the text book 		Midterm
10/27 Multi-class (softmax)
Parameter Tuning 		Optimization Algorithms
Intro to CNN
11/03 Convolution Neural Network (CNN) KMeans

Staff

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

Office: IRB 2224
Office Hours: W 10 - 11 AM


Teaching Assistants


Name Email (at umd.edu)
Aakriti Agrawal agrawal5
Georgios Milis milis
Michael-Andrei Panaitescu-Liess mpanaite
Vinayak Gupta vinayakg
Gourab Saha gsaha567
Shweta Bhardwaj shweta12


Office Hours

Instructor: Wed 10:00 - 11:00 AM

Teaching Assistants

Day
Office hours (AVW 4140 )
Monday Gourab: 11:30 PM - 1:30 PM,
Vinayak: 2:30 - 4:30 PM
Tuesday George: 9:00 AM - 11:00 AM
Wednesday George: 9:00 AM - 11:00 AM
Gourab: 11:30 - 1:30 PM
Thursday Vinayak: 9:00 AM - 11:00 AM,
Shweta: 11:00 AM - 1:15 PM,
Shweta: 1:45 PM - 3:30 PM
Friday Aakriti: 9:00 AM - 11:00 AM
Michael: 11:00 AM - 3:00 PM
Aakriti: 3:00 - 4:00 PM
Aakriti: 4:30 - 5:30 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*

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