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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)
TAs: Daniel McKee (dbmckee2 -at- illinois.edu), |
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:
Date | Topic | 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 networks, backpropagation: PPTX, PDF | |
September 25 | Backpropagation cont. | |
September 27 | No lecture, TA office hour in classroom | Assignment 1 due |
October 2 | Convolutional networks: PPTX, PDF | Assignment 2 out |
October 4 | Convolutional networks cont. | |
October 9 | Advanced training: PPTX, PPTX | |
October 11 | PyTorch tutorial: IPYNB | Project proposals due (for 4 credits) |
October 16 | Object detection: PPTX, PDF | Assignment 2 due |
October 18 | Object detection cont. | Assignment 3 Part 1 out |
October 23 | Dense prediction: PPTX, PDF | |
October 25 | Dense prediction cont. | Assignment 3 Part 2 out |
October 30 | Visualization: PPTX, PDF | |
November 1 | Adversarial examples: PPTX, PDF | |
November 6 | Generative adversarial networks: PPTX, PDF | |
November 8 | Conditional GANs: PPTX, PDF | |
November 13 | Recurrent networks: PPTX, PDF | |
November 15 | Recurrent networks cont. | Assignment 3 and project progress reports due Nov. 16 Assignment 4 out |
November 27 | Sequence-to-sequence models with attention: PPTX, PDF | |
November 29 | Deep Q-learning: PPTX, PDF | |
December 4 | Policy gradient methods: PPTX, PDF | Assignment 4 due Assignment 5 out |
December 6 | Deep RL applications: PPTX, PDF | |
December 11 | Project presentations, wrap-up | Assignment 5 and final project reports due Dec. 20 -- no late submisions! |