Fall 2018 CS 498 Introduction to Deep Learning

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This course will provide an elementary hands-on introduction to neural networks and deep learning. Topics covered will include linear classifiers, multi-layer neural networks, back-propagation and stochastic gradient descent, convolutional neural networks, recurrent neural networks, generative networks, and deep reinforcement learning. Coursework will consist of programming assignments in Python (primarily PyTorch). Those registered for 4 credit hours will have to complete a project.

Lectures: Tuesdays and Thursdays, 3:30PM-4:45PM, 1310 DCL

Instructor: Svetlana Lazebnik (slazebni -at- illinois.edu)
Office hours: 2-3PM Tuesdays and Thursdays, 3308 Siebel.

TAs: Daniel McKee (dbmckee2 -at- illinois.edu),
Maghav Kumar (mkumar10 -at- illinois.edu)
TA office hours: 3-4:30PM Mondays and Wednesdays, by the whiteboard outside 3407 Siebel.

Always check Piazza for last-minute changes to office hours!

Contacting the course staff: For emergencies and special circumstances (including extension requests), please email the instructor. For questions about lectures and assignments, use Piazza. For questions about your scores (including regrade requests), email the responsible TAs.

Prerequisites: Multi-variable calculus, linear algebra, data structures (CS 225 or equivalent), CS 361 or STAT 400. No previous exposure to machine learning is required.

Recommended textbook: I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.

Grading scheme:

  Be sure to read the course policies!

Schedule (tentative)

Date Topic Readings and assignments
August 28 Introduction: PPTX, PDF  
August 30 Statistical learning, kNN, linear classifiers: PPTX, PDF  
September 4 Linear classifiers part I: PPTX, PDF  
September 6 Linear classifiers part II: PPTX, PDF  
September 11 No class  
September 13 Python/numpy tutorial: ZIP Assignment 1 out
September 18 Linear models review: PPTX, PDF
Multi-class classification: PPTX, PDF
Bias-variance tradeoff, validation: PPTX, PDF
 
September 20 Multi-layer nets, backpropagation: PPTX, PDF  
September 25 Convolutional networks  
September 27 PyTorch tutorial Assignment 1 due
October 2 Convolutional networks cont.  
October 4 Advanced training (Adam, etc.)  
October 9 Advanced training cont. (Dropout, BatchNorm, etc.)  
October 11 Tutorial II Project proposals due (for 4 credits)
October 16 Detection and segmentation  
October 18 Detection and segmentation cont.
October 23 Detection and segmentation cont.  
October 25 Generative networks  
October 30 Generative networks cont.  
November 1 Visualization  
November 6 Adversarial examples  
November 8 Recurrent networks  
November 13 Recurrent networks cont.  
November 15 Image-text models  
November 27 Deep reinforcement learning  
November 29 Deep RL cont.  
December 4 Deep RL cont.  
December 6 Advanced topics
December 11 Advanced topics  

Resources

Other deep learning courses

Tutorials

Useful textbooks available online