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 |
|---|---|---|
| 08/26 | Course Intro
Welcome to Machine Learning
|
Decision Trees Reading: Chapter 1 of the text book |
| 09/02 | Decision Trees Contd. / Ensemble learning |
K-Nearest Neighbors Reading: Chapter 3 of the text book |
| 09/09 | Perceptron Reading: Chapter 4 of the text book Convex Review |
Convergence Analysis of Perceptron Reading: Chapter 4 of the text book Convex Review |
| 09/16 | Linear Classifiers and loss functions
Reading: Chapter 7 of the text book |
Gradient Descent
Reading: Chapter 7 of the text book |
| 09/23 | Naive Bayes Classifier Reading: Chapter 9 of the text book |
Logistic Regression Reading: Part II of notes |
| 09/30 | Logistic Regression Contd. | Binary to Multi-label Classification (OVR & AVA) |
| 10/07 | Multi-class (softmax) | Midterm (10/10) |
| 10/14 | Neural Networks Reading: Chapter 10 of the text book |
Neural Networks Contd. (Forward Prop) Reading: Chapter 10 of the text book |
| 10/21 | Back Propagation Reading: Chapter 10 of the text book |
Multi Label Classification Contd. Parameter Tuning Reading: Chapter 10 of the text book |
| 10/28 | Convolution Neural Network (CNN) | CNN Contd., KMeans |
| 11/04 | K-Means Contd. | Principal Components Analysis (PCA) |
| 11/11 | Dimensionality Reduction Contd. Intro to AutoEncoders Reading: Chapter 11 of the text book |
AutoEncoders & Kernels (SVM) Reading: Chapter 11 of the text book |
| 11/18 | More SVM / Recurrent Neural Networks (RNN) |
RNN / LSTM |
| 11/25 | No class (video lecture) | Thanksgiving Break |
| 12/02 | Intro to Generative AI | Variational Autoencoders |
Instructor: Mohammad Nayeem Teli (nayeem at cs.umd.edu)
Office: IRB 2224
Office Hours: W 10 - 11 AM
| Name | Email (at umd.edu) |
|---|---|
| Yongyuan Liang | cheryunl |
| Georgios Milis | milis |
| Michael-Andrei Panaitescu-Liess | mpanaite |
| Tuxun Lu | tuxunlu |
| Amirmahdi Namjoo | namjoo |
| Monday | Tuxun: 10:00 AM - 12:00 PM, Georgios: 3:00 - 5:00 PM |
| Tuesday | Yongyuan: 11:00 AM - 12:00 PM Amirmahdi: 1:00 - 3:00 PM |
| Wednesday |
Yongyuan: 12:00 - 2:00 PM Michael: 3:00 - 5:00 PM |
| Thursday |
Yongyuan: 11:00 AM - 12:00 PM Amirmahdi: 1:00 - 3:00 PM |
| Friday |
Georgios: 10:00 AM - 12:00 PM Tuxun: 1:00 - 3:00 PM Michael: 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* |
|---|