|Term||Fall 2017||Instructor||Christopher De Sa|
|Course website||www.cs.cornell.edu/courses/cs6787/2017fa/||[email hidden]|
|Schedule||MW 7:30pm – 8:45pm||Office hours||W 2:00pm – 3:00pm or by appointment|
|Room||Bill and Melinda Gates Hall G01||Office||Bill and Melinda Gates Hall 450|
Description: So you've taken a machine learning class. You know the models people use to solve their problems. You know the algorithms they use for learning. You know how to evaluate the quality of their solutions.
But when we look at a large-scale machine learning application that is deployed in practice, it's not always exactly what you learned in class. Sure, the basic models, the basic algorithms are all there. But they're modified a bit, in a bunch of different ways, to run faster and more efficiently. And these modifications are really important—they often are what make the system tractable to run on the data it needs to process.
CS6787 is a graduate-level introduction to these system-focused aspects of machine learning, covering guiding principles and commonly used techniques for scaling up to large data sets. Informally, we will cover the techniques that lie between a standard machine learning course and an efficient systems implementation. Topics will include stochastic gradient descent, acceleration, variance reduction, methods for choosing metaparameters, parallelization within a chip and across a cluster, popular ML frameworks, and innovations in hardware architectures. An open-ended project in which students apply these techniques is a major part of the course.
Prerequisites: Knowledge of machine learning at the level of CS4780. Optionally, knowledge of computer systems and hardware on the level of CS 3410 would be useful, but this is not a prerequisite.
Format: For half of the classes, typically on Mondays, there will be a traditionally formatted lecture. For the other half of the classes, typically on Wednesdays, we will read and discuss a seminal paper relevant to the course topic. These classes will involve a presentation by a group of students of the paper contents (each student will sign up in a group to present one paper) followed by breakout discussions about the material.
Grading: Students will be evaluated on the following basis.
|10%||In-class quizzes — there will be a quiz before each paper presentation on that paper's content|
|30%||Paper reviews — students must submit a review of every paper we discuss|
Paper review parameters: Paper reviews should be about one page (single-spaced) in length. The review guidelines should mirror what an actual conference review would look like (although you needn't assign scores or anything like that). In particular you should at least: (1) summarize the paper, (2) discuss the paper's strengths and weaknesses, and (3) discuss the paper's impact. For reference, you can read the NIPS reviewer guidelines, starting with the Overview section on page 6. Of course, your review will not be precisely like a real review, in large part because we already know the impact of these papers.
Final project parameters and course calendar may be subject to change.
Final project parameters: [Project Overview Slides] The final project can be done in groups of up to three (although more work will be expected from groups with more people). The subject of the project is open-ended, but it must include:
|Wednesday, August 23||No in-person lecture. I am traveling this week. Do not go to the lecture room. No one will be there.|
|Monday, August 28||Lecture 1. [Slides] [Demo Notebook] Topics:
|Wednesday, August 30||
Due: Sign-up for paper presentations.
Lecture 2. [Slides] [Demo Notebook] Topics:
|Monday, September 4||Labor day. No lecture.|
|Wednesday, September 6||Paper Discussion 1. Rich Caruana, Steve Lawrence, and C Lee Giles. Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. In Advances in neural information processing systems, pages 402–408, 2001|
|Monday, September 11||
Due: Review of Paper 1.
Lecture 3. [Slides] [Demo Notebook] Topics:
|Wednesday, September 13||Paper Discussion 2. Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. On the importance of initialization and momentum in deep learning. In International conference on machine learning, pages 1139–1147, 2013|
|Monday, September 18||
Due: Review of Paper 2.
Lecture 4. [Slides] Topics:
|Wednesday, September 20||Paper Discussion 3. Ali Rahimi and Benjamin Recht. Random features for large-scale kernel machines. In Advances in neural information processing systems, pages 1177–1184, 2007|
|Monday, September 25||
Due: Review of Paper 3.
Lecture 5. [Slides] Topics:
|Wednesday, September 27||Paper Discussion 4. Rie Johnson and Tong Zhang. Accelerating stochastic gradient descent using predictive variance reduction. In Advances in neural information processing systems, pages 315–323, 2013|
|Monday, October 2||
Due: Review of Paper 4.
Lecture 6. [Slides] [Demo Notebook] Topics:
|Wednesday, October 4||Paper Discussion 5. James Bergstra and Yoshua Bengio. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb): 281–305, 2012|
|Monday, October 9||Fall break. No lecture.|
|Wednesday, October 11||Due: Review of Paper 5. Paper Discussion 6. Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical bayesian optimization of machine learning algorithms. In Advances in neural information processing systems, pages 2951–2959, 2012|
|Monday, October 16||
Due: Review of Paper 6.
Lecture 7. [Slides] Topics:
|Wednesday, October 18||Paper Discussion 7. Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014|
|Monday, October 23||
Due: Review of Paper 7.
Lecture 8. [Slides] Topics:
|Wednesday, October 25||Paper Discussion 8. Feng Niu, Benjamin Recht, Christopher Re, and Stephen Wright. Hogwild: A lock-free approach to parallelizing stochastic gradient descent. In Advances in neural information processing systems, pages 693–701, 2011|
|Monday, October 30||
Due: Review of Paper 8.
Lecture 9. [Slides] Topics:
|Wednesday, November 1||Paper Discussion 9. Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. Deep learning with limited numerical precision. In International conference on machine learning, 2015|
|Monday, November 6||
Due: Review of Paper 9.
Lecture 10. [Slides] Topics:
|Wednesday, November 8||Paper Discussion 10. Song Han, Huizi Mao, and William J Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. ICLR, 2016|
|Monday, November 13||
Due: Review of Paper 10.
Due: Final Project Proposal.
Lecture 11. [Slides] Topics:
|Wednesday, November 15||Paper Discussion 11. Norman P Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al. In-datacenter performance analysis of a tensor processing unit. In Proceedings of the 44th Annual International Symposium on Computer Architecture, pages 1–12. ACM, 2017|
|Monday, November 20||
Due: Review of Paper 11.
Lecture 12. [Slides] Topics:
|Wednesday, November 22||Thanksgiving break. No lecture.|
|Monday, November 27||Due: Abstract for Final Project. Abstract swap and discussion.|
|Wednesday, November 29||
Lecture 14. [Slides] Topics:
|Wednesday, December 6||Due: Final Project Report.|