This course is an elementary introduction to a machine learning technique called deep learning, as well as its applications to a variety of domains. Along the way, the course also provides an intuitive introduction to machine learning such as simple models, learning paradigms, optimization, overfitting, importance of data, training caveats, etc. The assignments explore key concepts and simple applications, and the project(s) allows an in-depth exploration of a particular application area. By the end of the course, you will have an overview on the deep learning landscape and its applications. You will also have a working knowledge of several types of neural networks, be able to implement and train them, and have a basic understanding of their inner workings.
| Week Starting | Tuesday | Thursday |
|---|---|---|
| 01/27 | Snow Week (No class) | |
| 02/02 | Course Introduction
Motivation, Goals, Syllabus, Assignments Policies |
Introduction to Statistical Learning Simple models |
| 02/09 | Simple Models / Paradigms of learning |
Intro to Neural Networks |
| 02/16 | Neural Networks II |
Neural Networks III (Forward propagation) |
| 02/23 | Neural Networks IV (Forward propagation, Labels & Losses) |
Neural Networks V (Softmax Intro, Cross Entropy loss) |
| 03/02 | Optimization I |
Optimization II |
| 03/09 | Optimization III |
Midterm |
| 03/16 | Spring Break | |
| 03/23 | Optimization Contd. | Back Propagation |
| 03/30 | Optimization & Parameter tuning | PyTorch tutorial |
| 04/06 | Convolutional Neural Networks (CNN) | CNN Contd. |
| 04/14 | CNN Architectures | CNN Architectures Contd. |
| 04/21 | Object Detection | Object Detection Contd. |
Instructor: Mohammad Nayeem Teli (nayeem at umd.edu)
Office: IRB 2224
Office Hours: M 2 - 3 PM
| Name | Email (at umd.edu) |
|---|---|
| Vinayak Gupta | vinayakg |
| Alexander Stein | astein0 |
| Matthew Walmer | mwalmer |
| Siddhi Patil | scpatil |
| Gihan Chanaka Jayatilaka | gihan |
| Monday |
Matt: 11:00 AM - 1:00 PM Matt: 3:30 PM - 5:30 PM |
| Tuesday | Gihan: 9:00 AM - 11:00 AM Siddhi: 11:00 AM - 1:00 PM Alex: 10:00 AM - 2:00 PM |
| Wednesday |
Gihan: 11:30 AM - 1:30 PM Siddhi: 3:30 PM - 5:30 PM |
| Thursday |
Vinayak: 10:30 AM - 12:30 PM Vinayak: 3:30 PM - 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.
| Homework | Due Date* |
|---|