Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016"
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− | + | * Thurs Sep 1, 2016 [[Class meeting for 10-605 Overview|Overview]] Grading policies and etc, History of Big Data, Complexity theory and cost of important operations | |
− | + | * Tues Sep 6, 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 8, 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 | + | ** '''Start work on''' assignment 1a: streaming NB |
− | + | * Tues Sep 13, 2016 [[Class meeting for 10-605 Hadoop Overview|Hadoop Overview]] Intro to Hadoop, Hadoop Streaming | |
− | * | + | ** '''Start work on''' assignment 1b: streaming NB on streaming hadoop |
− | * Tues Sep 13 | + | * Thurs Sep 15, 2016 [[Class meeting for 10-605 Workflows For Hadoop 1|Workflows For Hadoop 1]] Scalably using out-of-memory-scale classifiers, Abstracts for map-reduce algorithms, Joins in Hadoop, TFIDF in Pig, Guinea Pig intro, TFIDF in Guinea Pig, Similarity joins |
− | ** | + | * Tues Sep 20, 2016 [[Class meeting for 10-605 Workflows For Hadoop 2|Workflows For Hadoop 2]] Similarity joins with TFIDF, Parallel simjoins, PageRank in Pig, K-means in Pig, Spark, Systems built on top of Hadoop |
− | * | + | ** '''Start work on''' assignment 2: naive bayes testing in guinea pig |
− | + | * Thurs Sep 22, 2016 [[Class meeting for 10-605 Phrase Finding|Phrase Finding]] Phrase-finding in Pig, Other work with phrases | |
− | + | * Tues Sep 27, 2016 [[Class meeting for 10-605 SGD and Hash Kernels|SGD and Hash Kernels]] Learning as optimization, Logistic regression with SGD, Regularized SGD, Hash kernels for logistic regression | |
− | + | * Thurs Sep 29, 2016 [[Class meeting for 10-605 Parallel Perceptrons 1|Parallel Perceptrons 1]] Debugging ML algorithms | |
− | * Tues Sep 20 | + | ** '''Start work on''' assignment 3: scalable sgd system |
− | + | * Thurs Oct 6, 2016 [[Class meeting for 10-605 Parallel Perceptrons 2|Parallel Perceptrons 2]] Structured perceptrons, Interative parameter mixing paper | |
− | + | * Tues Oct 11, 2016 [[Class meeting for 10-605 SGD for MF|SGD for MF]] Matrix factorization, Matrix factorization with SGD, distributed matrix factorization with SGD | |
− | ** | + | * Thurs Oct 13, 2016 [[Class meeting for 10-605 Midterm review|Midterm review]] |
− | * | + | ** '''Last assignment due''' |
− | * | + | * Tues Oct 18, 2016 [[Class meeting for 10-605 Midterm|Midterm]] |
− | + | * Thurs Oct 20, 2016 [[Class meeting for 10-605 Subsampling a Graph|Subsampling a Graph]] Sampling a graph, Local partitioning | |
− | + | ** '''Start work on''' assignment 4: graph subsampling | |
− | + | * Tues Oct 25, 2016 [[Class meeting for 10-605 Deep Learning 1|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 [[Class meeting for 10-605 Deep Learning 2|Deep Learning 2]] Reverse-mode differentiation, Recursive ANNs, Word2vec | |
− | + | * Tues Nov 1, 2016 [[Class meeting for 10-605 Randomized Algorithms 1|Randomized Algorithms 1]] Bloom filters, The countmin sketch | |
− | * | + | ** '''Start work on''' assignment 5: autodiff with IPM |
− | + | * Thurs Nov 3, 2016 [[Class meeting for 10-605 Randomized Algorithms 2|Randomized Algorithms 2]] Locality sensitive hashing | |
− | * | + | * Tues Nov 8, 2016 [[Class meeting for 10-605 Graph Architectures for ML|Graph Architectures for ML]] Graph-based ML architectures, Pregel, Signal-collect, GraphLab, PowerGraph, GraphChi, GraphX |
− | * | + | * Thurs Nov 10, 2016 [[Class meeting for 10-605 SSL on Graphs|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 [[Class meeting for 10-605 Unsupervised Learning On Graphs|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: graphX for SSL | |
− | + | * Thurs Nov 17, 2016 [[Class meeting for 10-605 Parameter Servers|Parameter Servers]] | |
− | + | * Tues Nov 22, 2016 [[Class meeting for 10-605 LDA 1|LDA 1]] DGMs for naive Bayes, Gibbs sampling for LDA | |
− | ** | + | ** '''Start work on''' assignment 7: LDA with parameter servers |
− | + | * Tues Nov 29, 2016 [[Class meeting for 10-605 LDA 2|LDA 2]] Parallelizing LDA, Fast sampling for LDA, DGMs for graphs | |
− | * Tues Oct 18 | + | * Thurs Dec 1, 2016 [[Class meeting for 10-605 Scalable Probabilistic Logics|Scalable Probabilistic Logics]] |
− | + | * Tues Dec 6, 2016 [[Class meeting for 10-605 Review session for final|Review session for final]] | |
− | * | + | ** '''Last assignment due''' |
− | + | * Thurs Dec 8, 2016 [[Class meeting for 10-605 Final Exam|Final Exam]] | |
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Revision as of 13:49, 11 August 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.
note: this is under construction
- Thurs Sep 1, 2016 Overview Grading policies and etc, History of Big Data, Complexity theory and cost of important operations
- Tues Sep 6, 2016 Probability Review Counting for big data and density estimation, streaming Naive Bayes, Rocchio and TFIDF
- Thurs Sep 8, 2016 Streaming Naive Bayes Notes on scalable naive bayes, Local counting in stream and sort
- Start work on assignment 1a: streaming NB
- Tues Sep 13, 2016 Hadoop Overview Intro to Hadoop, Hadoop Streaming
- Start work on assignment 1b: streaming NB on streaming hadoop
- Thurs Sep 15, 2016 Workflows For Hadoop 1 Scalably using out-of-memory-scale classifiers, Abstracts for map-reduce algorithms, Joins in Hadoop, TFIDF in Pig, Guinea Pig intro, TFIDF in Guinea Pig, Similarity joins
- Tues Sep 20, 2016 Workflows For Hadoop 2 Similarity joins with TFIDF, Parallel simjoins, PageRank in Pig, K-means in Pig, Spark, Systems built on top of Hadoop
- Start work on assignment 2: naive bayes testing in guinea pig
- 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 system
- 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: graph subsampling
- 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
- 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: graphX for SSL
- 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 parameter servers
- 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