Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016"
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* Tues Sep 13, 2016 [[Class meeting for 10-605 Hadoop Overview|Hadoop Overview]] Intro to Hadoop, Hadoop Streaming | * 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 | ** '''Start work on''' assignment 1b: streaming NB on streaming hadoop | ||
− | * Thurs Sep 15, 2016 [[Class meeting for 10-605 Workflows For Hadoop | + | * Thurs Sep 15, 2016 [[Class meeting for 10-605 Workflows For Hadoop|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 |
− | * Tues Sep 20, 2016 [[Class meeting for 10-605 Workflows For Hadoop | + | * Tues Sep 20, 2016 [[Class meeting for 10-605 Workflows For Hadoop|Workflows For Hadoop 2]] Similarity joins, 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 | ** '''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 | * 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 | * 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 | + | * Thurs Sep 29, 2016 [[Class meeting for 10-605 Parallel Perceptrons|Parallel Perceptrons 1]] Debugging ML algorithms |
** '''Start work on''' assignment 3: scalable sgd system | ** '''Start work on''' assignment 3: scalable sgd system | ||
− | * Thurs Oct 6, 2016 [[Class meeting for 10-605 Parallel Perceptrons | + | * Thurs Oct 6, 2016 [[Class meeting for 10-605 Parallel Perceptrons|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 | * 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]] | * Thurs Oct 13, 2016 [[Class meeting for 10-605 Midterm review|Midterm review]] | ||
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* Thurs Oct 20, 2016 [[Class meeting for 10-605 Subsampling a Graph|Subsampling a Graph]] Sampling a graph, Local partitioning | * 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 | ** '''Start work on''' assignment 4: graph subsampling | ||
− | * Tues Oct 25, 2016 [[Class meeting for 10-605 Deep Learning | + | * Tues Oct 25, 2016 [[Class meeting for 10-605 Deep Learning|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 | + | * Thurs Oct 27, 2016 [[Class meeting for 10-605 Deep Learning|Deep Learning 2]] Reverse-mode differentiation, Recursive ANNs, Word2vec |
− | * Tues Nov 1, 2016 [[Class meeting for 10-605 Randomized Algorithms|Randomized Algorithms]] | + | * Tues Nov 1, 2016 [[Class meeting for 10-605 Randomized Algorithms|Randomized Algorithms 1]] Bloom filters, The countmin sketch |
− | * Thurs Nov 3, 2016 [[Class meeting for 10-605 Randomized Algorithms|Randomized Algorithms]] Locality sensitive hashing | + | ** '''Start work on''' assignment 5: autodiff with IPM |
+ | * Thurs Nov 3, 2016 [[Class meeting for 10-605 Randomized Algorithms|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 | * 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 | * 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 | ||
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** '''Start work on''' assignment 6: graphX for SSL | ** '''Start work on''' assignment 6: graphX for SSL | ||
* Thurs Nov 17, 2016 [[Class meeting for 10-605 Parameter Servers|Parameter Servers]] | * Thurs Nov 17, 2016 [[Class meeting for 10-605 Parameter Servers|Parameter Servers]] | ||
− | * Tues Nov 22, 2016 [[Class meeting for 10-605 LDA | + | * Tues Nov 22, 2016 [[Class meeting for 10-605 LDA|LDA 1]] DGMs for naive Bayes, Gibbs sampling for LDA |
** '''Start work on''' assignment 7: LDA with parameter servers | ** '''Start work on''' assignment 7: LDA with parameter servers | ||
− | * Tues Nov 29, 2016 [[Class meeting for 10-605 LDA | + | * Tues Nov 29, 2016 [[Class meeting for 10-605 LDA|LDA 2]] Parallelizing LDA, Fast sampling for LDA, DGMs for graphs |
* Thurs Dec 1, 2016 [[Class meeting for 10-605 Scalable Probabilistic Logics|Scalable Probabilistic Logics]] | * 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]] | * Tues Dec 6, 2016 [[Class meeting for 10-605 Review session for final|Review session for final]] | ||
** '''Last assignment due''' | ** '''Last assignment due''' | ||
* Thurs Dec 8, 2016 [[Class meeting for 10-605 Final Exam|Final Exam]] | * Thurs Dec 8, 2016 [[Class meeting for 10-605 Final Exam|Final Exam]] |
Revision as of 16:54, 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.
- No classes will be held on Oct 4 (Rosh Hashana) or Nov 24 (Thanksgiving)
Schedule:
- 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 Scalable classification, Scalable Rocchio and TFIDF, Abstracts for map-reduce algorithms, Joins in Hadoop, TFIDF in Pig, Guinea Pig intro, TFIDF in Guinea Pig
- Tues Sep 20, 2016 Workflows For Hadoop 2 Similarity joins, 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