|Term||Fall 2019||Instructor||Christopher De Sa|
|Course website||www.cs.cornell.edu/courses/cs6787/2019fa/||[email hidden]|
|Schedule||MW 7:30pm – 8:45pm||Office hours||W 2:00pm – 3:00pm or by appointment|
|Room||Upson Hall 142||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 learning to large data sets. Informally, we will cover the techniques that lie between a standard machine learning course and an efficient systems implementation: both statistical/optimization techniques based on improving the convergence rate of learning algorithms and techniques that improve performance by leveraging the capabilities of the underlying hardware. Topics will include stochastic gradient descent, acceleration, variance reduction, methods for choosing hyperparameters, 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. If you are an undergraduate, you should have taken CS4780, since it is a prerequisite. 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 two seminal papers relevant to the course topic. These classes will involve presentations by groups of students of the paper contents (each student will sign up in a group to present one paper for 15-20 minutes) followed by breakout discussions about the material.
Final project parameters and course calendar may be subject to change.
Grading: Students will be evaluated on the following basis.
|20%||Paper reviews — students must submit a review for every pair of papers we discuss, but not for the week when they presented a paper|
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 "Review content" section. Of course, your review will not be precisely like a real review, in large part because we already know the impact of these papers. You can submit any review up to two days late with no penalty. Students who presented a paper do not have to submit a review of that paper (although you can if you want).
Final project parameters (subject to change): 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:
|Monday, September 2||Labor day. No lecture.|
|Wednesday, September 4||Lecture 1. [Slides] [Demo Notebook] [Demo HTML]
|Monday, September 9||
Lecture 2. [Slides] [Demo Notebook] [Demo HTML]
|Wednesday, September 11||Paper Discussion 1a. Martin Abadi et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. Preliminary White Paper, 2015. Since this is a white paper and is a bit longer than what we'll usually be reading, we will cover Sections 1, 2, 4.1, 6, and 9 only. Paper Discussion 1b. 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 (NeurIPS), 2001.|
|Monday, September 16||
Due: Review of Paper 1a or 1b.
Lecture 3. [Slides] [Demo Notebook] [Demo HTML]
|Wednesday, September 18||Paper Discussion 2a. Sergey Ioffe, Christian Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning (ICML), 2015. Paper Discussion 2b. Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. On the importance of initialization and momentum in deep learning. Proceedings of the International Conference on Machine Learning (ICML), 2013.|
|Monday, September 23||
Due: Review of Paper 2a or 2b.
Lecture 4. [Slides] [Demo Notebook] [Demo HTML]
|Wednesday, September 25||Programming Assignment 1 released. Paper Discussion 3a. Ali Rahimi and Benjamin Recht. Random features for large-scale kernel machines. In Advances in Neural Information Processing Systems (NeurIPS), 2007. Paper Discussion 3b. Kilian Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford and Alex Smola. Feature Hashing for Large Scale Multitask Learning. Proceedings of the International Conference on Machine Learning (ICML), 2009.|
|Monday, September 30||
Due: Review of Paper 3a or 3b.
Lecture 5. [Slides] [Demo Notebook] [Demo HTML]
|Wednesday, October 2||Paper Discussion 4a. Justin Ma, Lawrence K. Saul, Stefan Savage and Geoffrey M. Voelker. Identifying Suspicious URLs: An Application of Large-Scale Online Learning. Proceedings of the International Conference on Machine Learning (ICML), 2009. Paper Discussion 4b. Rie Johnson and Tong Zhang. Accelerating stochastic gradient descent using predictive variance reduction. In Advances in Neural Information Processing Systems (NeurIPS), 2013.|
|Monday, October 7||
Due: Review of Paper 4a or 4b.
Lecture 6. [Slides] [Demo Notebook] [Demo HTML]
|Wednesday, October 9||Programming Assignment 1 due. Paper Discussion 5a. James Bergstra and Yoshua Bengio. Random search for hyper-parameter optimization. Journal of Machine Learning Research (JMLR), 2012. Paper Discussion 5b. Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems (NeurIPS), 2012.|
|Monday, October 14||Fall break. No lecture.|
|Wednesday, October 16||
Programming Assignment 2 released.
Due: Review of Paper 5a or 5b.
Lecture 7. [Slides]
|Monday, October 21||
Due: Final Project Proposal.
Lecture 8. [Slides] [Demo Notebook] [Demo HTML]
|Wednesday, October 23||Paper Discussion 6a. Ashia C Wilson and Rebecca Roelofs and Mitchell Stern and Nati Srebro and Benjamin Recht. The Marginal Value of Adaptive Gradient Methods in Machine Learning. In Advances in Neural Information Processing Systems (NeurIPS), 2017. Paper Discussion 6b. Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations (ICLR), 2015.|
|Monday, October 28||
Programming Assignment 2 due.
Due: Review of Paper 6a or 6b.
Lecture 9. [Slides]
|Wednesday, October 30||
Paper Discussion 7a. 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 (NeurIPS), 2011.
Paper Discussion 7b. Jeff Dean
|Monday, November 4||
Due: Review of Paper 7a or 7b.
Lecture 10. [Slides] [Demo Notebook] [Demo HTML]
|Wednesday, November 6||Paper Discussion 8a. Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. Deep learning with limited numerical precision. Proceedings of the International Conference on Machine Learning (ICML), 2015. Paper Discussion 8b. Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David. BinaryConnect: Training Deep Neural Networks with binary weights during propagations. In Advances in Neural Information Processing Systems (NeurIPS), 2015.|
|Monday, November 11||
Due: Review of Paper 8a or 8b.
|Wednesday, November 13||Paper Discussion 9a. Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, and Yixin Chen. Compressing Neural Networks with the Hashing Trick. Proceedings of the International Conference on Machine Learning (ICML), 2015. Paper Discussion 9b. Song Han, Huizi Mao, and William J Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. Proceedings of the International Conference on Learning Representations (ICLR), 2016.|
|Monday, November 18||
Due: Review of Paper 9a or 9b.
|Wednesday, November 20||Paper Discussion 10a. 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 (ISCA), 2017. Paper Discussion 10b. Martin Abadi et al. TensorFlow: A System for Large-Scale Machine Learning. USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.|
|Monday, November 25||
Due: Review of Paper 10a or 10b.
Lecture 13. [Slides] [Demo Notebook] [Demo HTML]
|Wednesday, November 27||Thanksgiving break. No lecture.|
|Monday, December 2||Class cancelled due to weather.|
|Wednesday, December 4||Due: Abstract for Final Project. Abstract swap and discussion.|
|Monday, December 9||
Lecture 15. Guest Lecture (Anil Damle)
|Tuesday, December 10||Due: Final Project Report.|