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 final project allows an indepth 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.
CSI 1121
Tuesday, Thursday 3:30pm  4:45pm
Abhinav Shrivastava
4238 IRB
abhinav@cs.umd.edu
Office hours: Tuesday, 5:00pm  6:00pm (or by email)
Sweta Agrawal
Office hours: Thursday, 5:00pm  6:00pm
Piazza
Web Accessibility
(more coming soon..)
Date  Topic  Slides  Notes & Assignments 

January 31 
warningClass canceled (see UMD alert). Syllabus will soon be adjusted accordingly.
Course Introduction
Motivation Goals, Syllabus, Assignments Policies 
slides 

Machine Learning Basics  I


February 5 February 7 
Introduction to Statistical Learning
Simple models Paradigms of learning 
slides  
Neural Networks Basics  I


February 7 February 14 
Introduction to Neural Networks
Terminology Simple Neural Networks Nonlinearities 
slides  
Machine Learning Basics  II


February 12 
Computational Infrastructure (TA Lecture)
Introduction to PyTorch Quick walkthrough: Colaboratory, Google Cloud Platform 
slides  
February 14 February 19 library_books 
Problem setup: labels and losses
Types of problems and labels Loss functions 
slides 
Assignment 1 out Submission format 
February 19 February 26 
Optimization basics
Loss function derivatives, minimasGradient descent, Stochastic gradient descent 
slides  
Neural Network Basics  II


February 21 
Implementation: Neural Network Runthrough (TA Lecture)
Handling data (images, text) Structuring a neural network and machine learning codebase Introduction to class challenges (or how to Assignment 2 and onwards) 
ipynb  
February 22 library_books 
Assignment 1 due  
March 5 March 7 
Training Neural Networks
InitializationBackpropagation Optimization & hyperparameters 
slides(1) slides(2) 

March 7 March 12 
Training Caveats (Neural Networks and ML models)
OverfittingBias/Variance tradeoff Optimization & hyperparameters 
(see above)  
March 7 March 12 
Improving Performance of Neural Networks
Optimization tips and tricksBest principles 
(see above)  
March 14  Midterm exam
In class


March 15 library_books 
Assignment 2 & 3 out 

March 19 March 21 
Spring Break  
Convolutional Neural Networks (ConvNets)


March 26 
Introduction to ConvNets
ConvolutionsPooling 
slides  
March 28 April 2 library_books 
ConvNet Architectures
Popular architectures (primarily images, brief overview of videos)Intuitions and keyinsights Design principles 
(see above)  Assignment 2 due 
March 31 library_books 
Assignment 3 due  
April 2 
Understanding ConvNets via Visualization
Visualizing intermediate features and outputsInverting ConvNets Saliency maps Visualizing neurons 
slides  
Applications of ConvNets


April 4 April 9 April 12 
Application I: Object Detection  slides  
April 12 April 16 
Application II: Dense Prediction  slides  
warning
The following schedule is tentative and likely to change
warning


??  Application III: Generative Models  
??  Application III: Generative Models (cont.)  
Recurrent Neural Networks (RNNs)


April 16  Introduction to Recurrent Networks  
April 18  Application I: Text/Language Applications  
Advanced Vision + Language Topics


April 23  Vision + Language (models, tasks, training)  
April 25  Embeddings (triplet loss, word embeddings, etc.)  
Reinforcement Learning


April 30  Introduction to Deep Reinforcement Learning  
May 2  Introduction to Deep Reinforcement Learning (cont.)  
Advanced Critical Topics


May 7  Adversarial Examples (attacks and defenses)  
May 9  Ethics and Bias  
Epilogue


May 14  Buffer Class & Conclusion  
TBD  Project Presentations (likely to be outside class hours)  
Wednesday May 22 
Final Exams 10:30am  12:30pm 
Minimum grade of C in CMSC330 and CMSC351; and 1 course with a minimum grade of C from (MATH240, MATH461); and permission of CMNSComputer Science department.
We will work extensively with probability, statistics, mathematical functions such as logarithms and differentiation, and linear algebra concepts such as vectors and matrices. You should be comfortable manipulating these concepts.
We will use of the Python programming language. It is assumed that you know or will quickly learn how to program in Python. The programming assignments will be oriented toward Unixlike operating systems. While it is possible to complete the course using other operating systems, you will be solely responsible for troubleshooting any issues you encounter.
If you are unsure that you have the required prerequisites, consult with the instructor.
Here's how you will be graded:
Collaboration: Students are expected to finish the homeworks by himself/herself, but discussion on the assignments is allowed (and encouraged). The people you discussed with on assignments should be clearly detailed: before the solution to each question, list all people that you discussed with on that particular question. In addition, each student should submit his/her own code and mention anyone he/she collaborated with.
Details will be announced soon.
Syllabus subject to change.
The course should be self contained, but if you need additional reading material, you can consult the following:
If you need reference/additional readings for statistical learning, you can consult the following:
Note that academic dishonesty includes not only cheating, fabrication, and plagiarism, but also includes helping other students commit acts of academic dishonesty by allowing them to obtain copies of your work. In short, all submitted work must be your own. Cases of academic dishonesty will be pursued to the fullest extent possible as stipulated by the Office of Student Conduct. It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit http://www.shc.umd.edu.
Any student who needs to be excused for an absence from a single lecture, recitation, or lab due to a medically necessitated absence shall:
Any student who needs to be excused for a Major Scheduled Grading Event, must provide written documentation of the illness from the Health Center or from an outside health care provider. This documentation must verify dates of treatment and indicate the time frame that the student was unable to meet academic responsibilities. No diagnostic information shall be given. The Major Scheduled Grading Events for this course include midterm and final exam. For class presentations, the instructor will help the student swap their presentation slot with other students.
It is also the student's responsibility to inform the instructor of any intended absences from exams and class presentations for religious observances in advance. Notice should be provided as soon as possible, but no later than the Monday prior to the the midterm exam, the class presentation date, and the final exam.
Any student eligible for and requesting reasonable academic accommodations due to a disability is requested to provide a letter of accommodation from the Office of Disability Support Services within the first three weeks of the semester.
You can find the university’s course policies here.