Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2017

This is the syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016.

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Notes:

• Homeworks, unless otherwise posted, will be due when the next HW comes out.
• Lecture notes and/or slides will be (re)posted around the time of the lectures.
• Classes are cancelled for Sept 21 (Rosh Hashana)
• No classes will be held on Nov 23 (Thanksgiving)

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Schedule for 805 projects:

• 11:59pm Sun 10/1: Initial 805 project proposal due.
• 11:59pm Sun 10/15: Final 805 project proposal due.
  • This is a revised writeup that will address any comments William raises from the initial proposal.
• 11:59pm Sun 11/12: Midterm 805 project report due.
• 1:30-2:50pm Tues 12/5: Project presentations (in class).
• 11:59pm Sun 12/10: Final 805 project writeup due.

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Tentative schedule for lectures and 605 assignments:

• Tues Aug 30, 2016 Overview. Grading policies and etc, History of Big Data, Complexity theory and cost of important operations
• Thurs Sep 1, 2016 Probability Review. Counting for big data and density estimation, streaming Naive Bayes, Rocchio a\

nd TFIDF

• • Start work on Assignment 1a: Streaming NB. Draft at http://www.cs.cmu.edu/~wcohen/10-605/assignments/hashtable-nb.pdf
• Tues Sep 6, 2016 Streaming Naive Bayes. Notes on scalable naive bayes, Local counting in stream and sort
• Thurs Sep 8, 2016 Hadoop Overview. Intro to Hadoop, Hadoop Streaming
  • Start work on Assignment 1b: Streaming NB on Hadoop. Draft at http://www.cs.cmu.edu/~wcohen/10-605/assignments/stream-nb.pdf, https://drive.google.com/file/d\

/0BzQQ-spWKjhUd0NXSTB6TW82LWM/view

• Tues Sep 13, 2016 Workflows For Hadoop 1. Scalable classification, Scalable Rocchio and TFIDF, Abstracts for map-reduce algorithms, Joins in Hadoop
• Thurs Sep 15, 2016 Workflows For Hadoop 2. TFIDF in Pig, Guinea Pig intro, TFIDF in Guinea Pig, Similarity joins,

Similarity joins with TFIDF, Parallel simjoins

• • Start work on Assignment 2: Naive bayes testing in Guinea Pig, draft at https://drive.google.com/file/d/0B-p8_eIVeEHFM1JOSGFWNFFJcU0/view
• Tues Sep 20, 2016 Workflows For Hadoop 3. PageRank in Pig, K-means in Pig, Spark
• Thurs Sep 22, 2016 Phrase Finding. Systems built on top of Hadoop, Phrase-finding in Pig, Other work with phrases
• Tues Sep 27, 2016 SGD and Hash Kernels. Learning as optimization, Logistic regression with SGD, Regularized SGD, H\

ash kernels for logistic regression

• Thurs Sep 29, 2016 Parallel Perceptrons 1. Debugging ML algorithms
  • Start work on Assignment 3: scalable SGD Draft at http://curtis.ml.cmu.edu/w/courses/images/8/86/Sgd_fall15.pdf
• Tues Oct 4, 2016 Parallel Perceptrons 2.
• Thurs Oct 6, 2016 Parallel Perceptrons 3. Structured perceptrons, Interative parameter mixing paper
• Tues Oct 11, 2016 SGD for MF. Matrix factorization, Matrix factorization with SGD, distributed matrix factorization with SGD
• Thurs Oct 13, 2016 Midterm review.
  • Last assignment due
• Tues Oct 18, 2016 Midterm.
• Thurs Oct 20, 2016 Subsampling a Graph. Sampling a graph, Local partitioning
  • Start work on Assignment 4: Subsampling a Graph with Approximate PageRank, draft at https://drive.google.com/file/d/0BzQQ-spWKjhUaWoyOFZHV21uUlU/view
• Tues Oct 25, 2016 Deep Learning 1. Deep learning intro, BackProp following Nielson, Expressiveness of MLPs, Deep learning

and GPUs, Exploding and vanishing gradients, Modern deep learning models

• Thurs Oct 27, 2016 Deep Learning 2. Reverse-mode differentiation, Some systems using autodiff, Details on Wengert lists,

Breakdown of xman.py, Recursive ANNs, Convolutional ANNs

• Tues Nov 1, 2016 Randomized Algorithms 1. Bloom filters, The countmin sketch
  • Start work on Assignment 5: Autodiff with IPM. This is a new assignment for Fall 2016.
• Thurs Nov 3, 2016 [[Class meeting for 10-605 Randomized Algorithms 2 - someday, redo the count-min stuff|Randomized Algorithms 2 - someday, redo the count-min stuff]\

]. Review of Bloom filters, Locality sensitive hashing

• Tues Nov 8, 2016 Graph Architectures for ML. Graph-based ML architectures, Pregel, Signal-collect, GraphLab,

PowerGraph, GraphChi, GraphX

• Thurs Nov 10, 2016 SSL on Graphs. Semi-supervised learning intro, Multirank-walk SSL method, Harmonic fields, Modified Ad\

sorption SSL method, MAD with countmin sketches

• Tues Nov 15, 2016 Unsupervised Learning On Graphs. Spectral clustering, Power iteration clustering, Label propagation for clustering non-graph data, Label propagation for SSL on non-graph data
  • Start work on Assignment 6: To be decided, possibly using Spark/GraphX to do PIC or MRW.
• Thurs Nov 17, 2016 Parameter Servers. Parameter servers, PS vs Hadoop, State Synchronous Parallel (SSP) model, Manage\

d Communication in PS, LDA Sampler with PS

• Tues Nov 22, 2016 LDA 1. DGMs for naive Bayes, Gibbs sampling for LDA
  • Start work on Assignment 7: LDA with a Parameter Server, draft http://curtis.ml.cmu.edu/w/courses/images/1/16/Hw7-lda-ps.pdf
• Thurs Nov 24, 2016 LDA 2. Parallelizing LDA, Fast sampling for LDA, DGMs for graphs
• Tues Nov 29, 2016 Review session for final.
  • Last assignment due
• Thurs Dec 1, 2016 Final Exam.