CS584: Deep Learning

Prof. Shamim Nemati (OH: Mon 1:00pm-2:00pm in BMI (36 Eagle Row, 5th Floor South) 579)
TF: Supreeth Prajwal (OH: Wed 10:15am-11:15am BMI 581)
TF: Ali Ahmadvand (OH: Tue 9am-10am BMI 581)
Time: Monday and Wednesday, 11:30am-12:45pm
Location: MSc E408
Contact: Instructor firstname dot Instructor lastname at emory.edu
Course Website: http://nematilab.info/CS584.html
GitHub: https://github.com/NematiLab/CS584 (permission only)
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Announcements


Schedule

Subject to change.
Wed 11 Jan 2017

Lecture 1: Introduction to Deep Learning


Wed 18 Jan 2017

Lecture 2: Intro to Deep Learning Software and Hardware

Feedforward Networks

Mon 23 Jan 2017

Lecture 3: Deep Feedforward Networks


Wed 25 Jan 2017

Lecture 4: Optimization


Mon 30 Jan 2017

Lecture 5: Convolutional Neural Networks

Wed 1 Feb 2017

Lecture 6: Convolutional Neural Networks

Time Series Models

Mon 6 Feb 2017

Lecture 7: Recurrent Neural Networks


Wed 8 Feb 2017

Lecture 8: Dynamic Bayesian Networks

Reinforcement Learning

Mon 13 Feb 2017

Lecture 9: Deep RL, Discrete Action Spaces


Wed 15 Feb 2017

Lecture 10: Deep RL, Continuous Action Spaces

Applications & Practicals

Mon 20 Feb 2017

Lecture 11: Deep Learning in Practice


Wed 22 Feb 2017

Lecture 12: Biomedical Applications

Unsupervised Deep Learning

Monday 27 Feb 2017

Lecture 13: Linear Factor Models


Mon 13 March 2017

Lecture 14: Autoencoders


Wed 15 March 2017

Lecture 15: Introduction to Boltzmann Machines


Mon 20 March 2017

Lecture 16: Unsupervised Time Series Modeling


Wed 22 March 2017

Midterm Project Presentations

Sampling and Inference Procedures

Mon 27 March 2017

Lecture 17: Sampling Techniques


Wed 29 March 2017

Lecture 18: Approximate Inference

Deep Generative Models

Mon 3 April 2017

Lecture 19: Restricted Boltzmann Machine


Wed 5 April 2017

Lecture 20: Recurrent Temporal RBM


Mon 10 April 2017

Lecture 21: Helmholtz Machines I


Wed 12 April 2017

Lecture 22: Helmholtz Machines II

Deep Learning research

Mon 17 April 2017


Mon 19 April 2017

Final Presentations and Reports

Mon 24, Wed 26 April 2017


May 1 2017

Assignments

Weekly Reports should be documented in one single
Google Doc , with clear headings for each week (dated). Please share the link with your instructor.
These assignments include your progress on hands-on coding tutorials, including any obstacles preventing you from successfully completing your tutorials.
Additionally, you're expected to review and critique one paper every week (minimum 300 words).
Finally, your weekly report should include any concepts that you find difficult to understand pertaining to the corresponding week's lectures.

  • [required] Assignment 1: Feedforward Neural Networks
  • [required] Assignment 2: Convolutional Neural networks
  • [required] Assignment 3: Recurrent Neural Networks
  • [required] Assignment 4: A Practical Application
  • [required] Assignment 5: Visualization
  • [required] Assignment 6: Autoencoders (AE)
  • [required] Assignment 7: Unsupervised Learning
  • [optional] Assignment 8: Generative Networks

  • Final Project

    Use
    overleaf to write a journal style report and share the link with your instructor. You may use the NIPS style files available here.

    Grading


    General Deep Learning and Machine Learning Books

    Other Resources


    Frequently Asked Questions