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.
| Week Starting | Tuesday | Thursday |
|---|---|---|
| 08/29 | Course Intro
Welcome to Machine Learning
|
Decision Trees Reading: Chapter 1 of the text book |
| 09/05 | Nearest Neighbor Classification Reading: Chapter 3 of the text book |
Perceptron Reading: Chapter 4 of the text book |
| 09/12 | Convergence Analysis of Perceptron |
Linear Classifiers, Gradient Descent and Hinge Loss Reading: Chapter 7 of the text book |
| 09/19 | Gradient Descent Part II Reading: Chapter 7 of the text book |
GD (part III)+ Probabilistic View of ML, Naive Bayes Reading: Chapter 9 of the text book |
| 09/26 | Naive Bayes Classifier | Logistic Regression Reading: Part II of notes |
| 10/03 | Logistic Regression br> Reading: Part II of notes |
Multi-label Classification< |
| 10/10 | Midterm | Neural Networks Reading: Chapter 10 of the text book |
| 10/17 | Nonlinear Regression + Back Propagation | Back Propagation Reading: Chapter 10 of the text book |
| 10/24 | Multi Label Classification, Vanishing Gradients, Momentum method Reading: Chapter 10 of the text book |
More Deep Neural networks, CNN |
| 10/31 | Adversarial Robustness + Recurrent Neural Networks | Unsupervised Learning + K-Means, PCA |
| 11/07 | PCA Analysis | AutoEncoders Reading: Chapter 11 of the text book |
| 11/14 | Kernels Reading: Chapter 11 of the text book |
Deep Learning frameworks / GANs |
| 11/21 | Gaussian Mixture Models Reading: Chapter 7 of the text book |
thanksgiving |
| 11/28 | Expectation Maximization Reading: Chapter 16 of the text book |
|
| 12/05 | Final Presentations I | Final Presentations II |
Instructor: Mohammad Nayeem Teli (nayeem at cs.umd.edu)
Office: IRB 1128
Office Hours: Tuesdays, 4:45 PM - 5:30 PM, IRB 1128
| Name | |
|---|---|
| John D Kanu | jdkanu at umd.edu |
| Tin Trung Nguyen | tintn at umd.edu |
| Tuesday | Tin: 1:00 - 3:00 PM |
| Wednesday | John: 3:00 - 5:00 PM |
| Thursday | Tin: 1:00 - 3:00 PM |
| Friday | John: 3:00 - 5:00 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.
| Homework | Due Date* |
|---|---|
| Homework 1: Warm Up | Tuesday September 06, 2022 |
| Homework 2: Decision Trees | Tuesday September 13, 2022 |
| Homework 3: High Dimensional Space | Thursday September 22, 2022 |
| Homework 4: Linear Models and Perceptron | Thursday September 29, 2022 |
| Homework 5: Multiclass Classification | Thursday October 20, 2022 |
| Project 1: Classification | Monday October 31, 2022 |
| Programming Assignment 1: Multiclass and Linear Models | Tuesday November 15, 2022 |
| Homework 6: Principal Component Analysis | Friday November 18, 2022 |
| Project 3: Principal Component Analysis | Saturday December 03, 2022 |
| Final Project Presentations | In class, December 6 and 8, 2022 |
| Final Project Presentations (pdf) on ELMS | Thursday December 8, 2022 |
| Final Project Report | Thursday December 15, 2022 |