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, March 13th, in Lecture.
  • Final Exam: Thursday, May 15, 10:30 AM - 12:30 PM, Location: TBA

Lectures (Tentative)


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Week Starting Tuesday Thursday
01/27 Course Intro
Welcome to Machine Learning

 Decision Trees

Reading: Chapter 1 of the text book
02/03 Decision Trees Contd. / Ensemble learning

 K-Nearest Neighbors

Reading: Chapter 3 of the text book
02/10 Perceptron
Reading: Chapter 4 of the text book
Convex Review 		Convergence Analysis of Perceptron

Reading: Chapter 4 of the text book
Convex Review
02/17 Convergence Proof
Linear Classifiers

Reading: Chapter 7 of the text book 		Linear Classifiers & Gradient Descent

Reading: Chapter 7 of the text book
02/24 Classifier based on Bayes' theorem Naive Bayes' Classifier

Reading: Chapter 9 of the text book
03/03 Naive Bayes' Classifier, Logistic Regression

Reading: Part II of notes 		Logistic Regression & GD updates
03/10 Class canceled Midterm (03/13)
03/17 Spring Break
03/24 Multi-class classification Neural Network

Reading: Chapter 10 of the text book
03/31 Neural Networks Contd. (Forward Prop)

Reading: Chapter 10 of the text book 		Back Propagation

Reading: Chapter 10 of the text book
04/07 Multi Label Classification Contd.


Reading: Chapter 10 of the text book 		Parameter Tuning
04/15 Parameter Tuning Contd.
Dimensionality Reduction 		Dimensionality Reduction Contd.

Staff

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

Office: IRB 2224
Office Hours: Th 3 - 4 PM


Teaching Assistants


Name Email (at umd.edu)
Pankayaraj Pathmanathan pan
Georgios Milis milis
Michael-Andrei Panaitescu-Liess mpanaite
Kazem Faghih mkfaghih
Osvaldo Luamba Quinjica quinjica


Office Hours

Instructor: Th 3:00 - 4:00 PM

Teaching Assistants

Day
Office hours (AVW 4140 )
Monday Osvaldo: 11:00 AM - 1:00 PM,
Kazem: 1:00 PM - 3:00 PM,
Georgios: 3:00 - 5:00 PM
Wednesday Kazem : 1:00 - 3:00 PM
Georgios: 3:00 - 5:00 PM
Thursday Michael: 3:30 PM - 5:30 PM
Friday Osvaldo: 11:00 AM - 1:00 PM,
Pankayaraj: 1:00 - 3:00

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.