Introduction to Deep Learning

CMSC498L · Spring 2020 · 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

ESJ 2204
Tuesday, Thursday 2:00pm - 3:15pm


Abhinav Shrivastava
Office hours: Tuesday, 4:00pm - 5:00pm (or by email)
4238 IRB

Teaching Assistant

Pulkit Kumar
Office hours: Thursday, 4:00pm - 5:00pm (or by email)
4232 IRB

Mara Levy (Tentative)

Quick Links

Web Accessibility


Date Topic Slides Notes & Assignments
January 28
January 30
Course Introduction
Goals, Syllabus, Assignments

Machine Learning Basics - I
January 30
Introduction to Statistical Learning
Simple models
Paradigms of learning
Neural Networks Basics - I
February 4
Introduction to Neural Networks
Simple Neural Networks
slides Assignment 1 out
February 6
Problem setup: labels and losses
Types of problems and labels
Loss functions
Computation Resources Overview
February 11
Computational Infrastructure (TA Lecture)
Quick walk-through: Colaboratory, Google Cloud Platform Introduction to PyTorch
iPython Assignment 1 due
February 13
Neural Network Run-through (TA Lecture)
Handling data (images, text)
Structuring a neural network and machine learning codebase
Introduction to class challenges
Machine Learning Basics - II
February 18
February 20
Optimization basics
Loss function derivatives, minimas
Gradient descent, Stochastic gradient descent
Neural Network Basics - II
Febuary 25
Febuary 27
Training Neural Networks
Optimization & hyperparameters
slides (1)
slides (2)
Febuary 27
Training Caveats (Neural Networks and ML models)
Bias/Variance trade-off
Optimization & hyperparameters
(see above)
March 3
Improving Performance of Neural Networks
Optimization tips and tricks
Best principles
(see above)
March 5
No Class
ECCV deadline (Wish us luck!)
March 10
Mid-term exam
In class
Convolutional Neural Networks (ConvNets)
March 12
March 31
Introduction to ConvNets
March 17
March 19
March 24
March 26
Extended Spring Break due to CoVid-19
March 31
April 2
April 7
ConvNet Architectures
Popular architectures (primarily images, brief overview of videos)
Intuitions and key-insights
Design principles
(see above)

Understanding ConvNets via Visualization
Visualizing intermediate features and outputs
Inverting ConvNets
Saliency maps
Visualizing neurons
Applications of ConvNets
April 7
April 9
Application I: Object Detection slides
April 9
April 14
Application II: Dense Prediction slides
Schedule below is tentative, and will evolve as we move to the virtual setup.
Recurrent Neural Networks (RNNs)
April 14
April 16
Introduction to Recurrent Networks
Text/Language Applications
Language Modelling
April 28
April 30
Introduction to Self-attention or Transformers
Self-attenton or Transformers
slides Bonus Assignment out
Advanced Topics
April 30
May 5
Vision + Language (models, tasks, training) (see above)
May 5
May 7
May 12
Image Generative Models
Auto regressive models
GANs, pix2pix, CycleGAN, etc.
Teasers: Text Generation, Self-supervised Learning
Self-study (A brief) Introduction to (Deep) Reinforcement Learning slides
May 12
Ethics and Bias; Epilogue slides
May 18
Final Exams



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:

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

Useful interactive textbooks/courses available online

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.