Introduction to Deep Learning

CMSC472 · Spring 2021 · Unive-remove-rsity of Maryland


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 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.


Where & when

Tuesday, Thursday 2:00pm - 3:15pm


Abhinav Shrivastava
Office hours: Tuesdays 4:00-5:00pm (or by email) (see Piazza for details).

Teaching Assistant

Vinoj Jayasundara
Office hours: Wednesday, 4:30-5:00pm

Matthew Gwilliam
Office hours: Monday, 1:30-2:00pm

Soumik Mukhopadhyay
Office hours: Friday, 10:00-11:00am (every other week)

Quick Links

Web Accessibility


Date Topic Slides Notes & Assignments
January 26
Course Introduction
Goals, Syllabus, Assignments

Machine Learning Basics - I
January 28
Introduction to Statistical Learning
Simple models
Paradigms of learning
Neural Networks Basics - I
February 2 library_books
Introduction to Neural Networks
Simple Neural Networks
slides Assignment 1 out
February 4
Problem setup: labels and losses
Types of problems and labels
Loss functions
Machine Learning Basics - II
February 9 library_books
February 11
Optimization basics
Loss function derivatives, minimas
Gradient descent, Stochastic gradient descent
slides Assignment 1 due
Computation Resources Overview
February 16
Computational Infrastructure (TA Lecture)
Implementation: Neural Network Basics and Run-through (TA Lecture)
Quick walk-through: Colaboratory, Google Cloud Platform
Introduction to PyTorch
Handling data (images, text)
Structuring a neural network and machine learning codebase
colab (1)
colab (2)
Neural Network Basics - II
Febuary 18
No class
Inclement Weather Guidance
Snow day
Febuary 23 library_books
Febuary 25
Training Neural Networks
Optimization & hyperparameters
slides (backprop)
slides (opt)
Assignment 2 out
March 2 library_books
March 4
Training Caveats (Neural Networks and ML models)
Bias/Variance trade-off
Optimization & hyperparameters

Improving Performance of Neural Networks
Optimization tips and tricks
Best principles
(see above) Assignment 2 due
Convolutional Neural Networks (ConvNets)
March 9
Introduction to ConvNets
March 11library_books
Midterm Exam
In class
Assignmnt 3 out
March 16
March 18
Spring Break
March 23
March 25 library_books
ConvNet Architectures
Popular architectures (primarily images, brief overview of videos)
Intuitions and key-insights
Design principles
(see above) Assignment 3 due
Applications of ConvNets
March 30library_books
April 1
April 6
Application I: Object Detection slides Project proposals due
April 6
April 8
Application II: Dense Prediction slides
Recurrent Neural Networks (RNNs)
April 13
April 15library_books
Introduction to Recurrent Networks
Text/Language Applications
Language Modeling
slides Assignment 4 due
April 15
April 20 library_books
Introduction to Self-attention or Transformers
Self-attenton or Transformers
slides Assignment 5 out
Advanced Topics
April 15
April 20
Vision + Language (models, tasks, training) (see above)
April 20
April 23
April 27library_books
April 29library_books
Image Generative Models
Auto regressive models
GANs, pix2pix, CycleGAN, etc.
Teasers: Text Generation, Self-supervised Learning
slides Assignment 5 due
Bonus Assignment 6 out
May 4
May 6library_books
(A brief) Introduction to (Deep) Reinforcement Learning slides Bonus Assignment 6 due
May 11 Ethics and Bias; Epilogue slides
May 17library_books
Final Exam
10:30am - 12:30pm (class link, live, virtual)



Minimum grade of C- in CMSC330 and CMSC351; and 1 course with a minimum grade of C- from (MATH240, MATH461); and permission of CMNS-Computer 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 Unix-like 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.

Grading and collaboration

Here's how you will be graded:

  • Assignments: 35%
  • Project (presentation/poster and report): 35%
  • Exams (1 mid-term, 1 final): 30%
  • Bonus points for top-3 ranks for each challenge assignment

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.

Submitting assignments and project reports, format, etc.

Details will be announced in class.


Syllabus subject to change.


The course should be self contained, but if you need additional reading material, you can consult the following:

Helpful review and reference material:

If you need reference/additional readings for statistical learning, you can consult the following:

Useful interactive textbooks/courses available online

Previous iterations of this course

Other deep learning courses

Linear algebra material that'll help

Tutorials (libraries and computation resources)

Accommodations and Policies

Late Days

  • You get a total of 7 late days (to be used in 24-hour blocks) which can be used throughout the course.
  • Each late day is bound to only one submission. For example, if one assignment and one report are submitted 3 hours after the deadline, this results in 2 late days being used.
  • Late days cannot be used for the final project presentation, the final project report, the final assignment, and any of the exams.
  • Once these late days are exhausted, any submissions turned in late will be penalized 25% per late day. Therefore, no submission will be accepted more than three days after its due date. Except where the submission is not eligible to utilize late days (see above), in which case the submission will be accepted after the deadline.
  • If you submit a submission multiple times, only the last one will be taken into account. If the last submission was after the deadline, late days will be deducted accordingly.
  • If you are unsure about the late day policy, contact the instructor before utilizing them. See below for exceptions.

Academic Integrity

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

Excused Absences and Academic Accommodations

Any student who needs to be excused for an absence from a single lecture, recitation, or lab due to a medically necessitated absence shall:

  • Make a reasonable attempt to inform the instructor of his/her illness prior to the class.
  • Upon returning to the class, present their instructor with a self-signed note attesting to the date of their illness. Each note must contain an acknowledgment by the student that the information provided is true and correct. Providing false information to University officials is prohibited under Part 9(i) of the Code of Student Conduct (V-1.00(B) University of Maryland Code of Student Conduct) and may result in disciplinary action.
  • This self-documentation may not be used for the Major Scheduled Grading Events as defined below.

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.

Other Accommodations and Policies

You can find the university’s course policies here.