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: TBA

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

Convergence Analysis of Perceptron

Reading: Chapter 4 of the text book
Convex Review
09/18 Linear Classifiers, Gradient Descent and Hinge Loss

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

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)


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