Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016"
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* Tues Aug 30, 2016 [[Class meeting for 10-605 Overview|Overview]]. Grading policies and etc, History of Big Data, Complexity theory and cost of important operations | * Tues Aug 30, 2016 [[Class meeting for 10-605 Overview|Overview]]. Grading policies and etc, History of Big Data, Complexity theory and cost of important operations | ||
* Thurs Sep 1, 2016 [[Class meeting for 10-605 Probability Review|Probability Review]]. Counting for big data and density estimation, streaming Naive Bayes, Rocchio and TFIDF | * Thurs Sep 1, 2016 [[Class meeting for 10-605 Probability Review|Probability Review]]. Counting for big data and density estimation, streaming Naive Bayes, Rocchio and TFIDF | ||
| − | ** '''Start work on''' Assignment 1a: Streaming NB. | + | ** '''Start work on''' Assignment 1a: Streaming NB. [http://www.cs.cmu.edu/~wcohen/10-605/assignments/2016-fall/hashtable-nb.pdf Writeup]. |
* Tues Sep 6, 2016 [[Class meeting for 10-605 Streaming Naive Bayes|Streaming Naive Bayes]]. Notes on scalable naive bayes, Local counting in stream and sort | * Tues Sep 6, 2016 [[Class meeting for 10-605 Streaming Naive Bayes|Streaming Naive Bayes]]. Notes on scalable naive bayes, Local counting in stream and sort | ||
* Thurs Sep 8, 2016 [[Class meeting for 10-605 Hadoop Overview|Hadoop Overview]]. Intro to Hadoop, Hadoop Streaming | * Thurs Sep 8, 2016 [[Class meeting for 10-605 Hadoop Overview|Hadoop Overview]]. Intro to Hadoop, Hadoop Streaming | ||
Revision as of 11:05, 1 September 2016
This is the syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016.
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.
- No classes will be held on Oct 4 (Rosh Hashana) or Nov 24 (Thanksgiving)
Schedule:
- 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 and TFIDF
- Start work on Assignment 1a: Streaming NB. Writeup.
- 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, TFIDF in Pig, Guinea Pig intro, TFIDF in Guinea Pig
- Thurs Sep 15, 2016 Workflows For Hadoop 2. 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, Systems built on top of Hadoop
- Thurs Sep 22, 2016 Phrase Finding. 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, Hash 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
- Thurs Oct 6, 2016 Parallel Perceptrons 2. 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, Deep learning and GPUs, Expressiveness of MLPs, Exploding and vanishing gradients, Modern deep learning models
- Thurs Oct 27, 2016 Deep Learning 2. Reverse-mode differentiation, Recursive ANNs, Word2vec
- 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 Randomized Algorithms 2. 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 Adsorption 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.
- 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
- Tues Nov 29, 2016 LDA 2. Parallelizing LDA, Fast sampling for LDA, DGMs for graphs
- Thurs Dec 1, 2016 Scalable Probabilistic Logics.
- Tues Dec 6, 2016 Review session for final.
- Last assignment due
- Thurs Dec 8, 2016 Final Exam.
