Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-405 in Spring 2018"

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** '''Previous assignment due'''
 
** '''Previous assignment due'''
 
* Wed Mar 7, 2018 [[Class meeting for 10-405 Midterm|Midterm]].   
 
* Wed Mar 7, 2018 [[Class meeting for 10-405 Midterm|Midterm]].   
−
* Mon Mar 19, 2018 [[Class meeting for 10-405 Computing with GPUs|Computing with GPUs]].  Introduction to GPUs, CUDA, Vectorization
+
* Mon Mar 19, 2018 [[Class meeting for 10-405 Deep Learning|Deep Learning 1]].  BackProp following Nielson, Deep learning and GPUs, Reverse-mode differentiation (autodiff)
−
** '''Start work on''' Assignment 4: Autodiff with IPM part 1/2; Draft at http://www.cs.cmu.edu/~wcohen/10-405/assignments/2016-fall/hw-5-autodiff/main.pdf
+
* Wed Mar 21, 2018 [[Class meeting for 10-405 Deep Learning|Deep Learning 2]].  Reverse-mode differentiation (autodiff), Some systems using autodiff, Details on Wengert lists, Breakdown of xman.py, Inputs, parameters, updates
−
* Wed Mar 21, 2018 [[Class meeting for 10-405 Deep Learning|Deep Learning 1]].  Deep learning intro, BackProp following Nielson, Expressiveness of MLPs, Deep learning and GPUs, Exploding and vanishing gradients, Modern deep learning models
+
** '''Start work on''' Assignment 4: Autodiff with IPM part 1/2; Draft at http://www.cs.cmu.edu/~wcohen/10-605/assignments/2017-fall/hw-4+5-autodiff/hw4/main.pdf
−
* Mon Mar 26, 2018 [[Class meeting for 10-405 Deep Learning|Deep Learning 2]].  Reverse-mode differentiation (autodiff), Some systems using autodiff, Details on Wengert lists, Breakdown of xman.py
+
* Mon Mar 26, 2018 [[Class meeting for 10-405 Deep Learning|Deep Learning 3]].  Expressiveness of MLPs, Exploding and vanishing gradients, Modern deep learning models, Word2vec and GloVE, Recursive ANNs, Convolutional ANNs, Achitectures using RNNs
−
* Wed Mar 28, 2018 [[Class meeting for 10-405 Deep Learning|Deep Learning 3]].  Inputs, parameters, updates, Word2vec and GloVE, Recursive ANNs, Convolutional ANNs, Achitectures using RNNs
+
* Wed Mar 28, 2018 [[Class meeting for 10-405 Computing with GPUs|Computing with GPUs]].  Introduction to GPUs, CUDA
 
* Mon Apr 2, 2018 [[Class meeting for 10-405 Randomized Algorithms|Randomized Algorithms 1]].  Bloom filters, The countmin sketch, CM Sketches in Deep Learning
 
* Mon Apr 2, 2018 [[Class meeting for 10-405 Randomized Algorithms|Randomized Algorithms 1]].  Bloom filters, The countmin sketch, CM Sketches in Deep Learning
 +
* Wed Apr 4, 2018 [[Class meeting for 10-405 Randomized Algorithms|Randomized Algorithms 2]].  Review of Bloom filters, Locality sensitive hashing, Online LSH
 
** '''Start work on''' Assignment 5: Autodiff with IPM part 2/2
 
** '''Start work on''' Assignment 5: Autodiff with IPM part 2/2
−
* Wed Apr 4, 2018 [[Class meeting for 10-405 Randomized Algorithms|Randomized Algorithms 2]].  Review of Bloom filters, Locality sensitive hashing, Online LSH
 
 
* Mon Apr 9, 2018 [[Class meeting for 10-405 Graph Architectures for ML|Graph Architectures for ML]].  Graph-based ML architectures, Pregel, Signal-collect, GraphLab, PowerGraph, GraphChi, GraphX
 
* Mon Apr 9, 2018 [[Class meeting for 10-405 Graph Architectures for ML|Graph Architectures for ML]].  Graph-based ML architectures, Pregel, Signal-collect, GraphLab, PowerGraph, GraphChi, GraphX
−
** '''Start work on''' Assignment 6: SSL in Spark
 
 
* Wed Apr 11, 2018 [[Class meeting for 10-405 SSL on Graphs|SSL on Graphs]].  Semi-supervised learning intro, Multirank-walk SSL method, Harmonic fields, Modified Adsorption SSL method, MAD with countmin sketches
 
* Wed Apr 11, 2018 [[Class meeting for 10-405 SSL on Graphs|SSL on Graphs]].  Semi-supervised learning intro, Multirank-walk SSL method, Harmonic fields, Modified Adsorption SSL method, MAD with countmin sketches
 +
** '''Start work on''' Assignment 6: SSL on a graph in Spark maybe using NELL data?
 
* Mon Apr 16, 2018 [[Class meeting for 10-405 LDA|LDA 1]].  DGMs for naive Bayes, Gibbs sampling for LDA
 
* Mon Apr 16, 2018 [[Class meeting for 10-405 LDA|LDA 1]].  DGMs for naive Bayes, Gibbs sampling for LDA
 
* Wed Apr 18, 2018 [[Class meeting for 10-405 LDA|LDA 2]].  Parallelizing LDA, Fast sampling for LDA, DGMs for graphs
 
* Wed Apr 18, 2018 [[Class meeting for 10-405 LDA|LDA 2]].  Parallelizing LDA, Fast sampling for LDA, DGMs for graphs
 
* Mon Apr 23, 2018 [[Class meeting for 10-405 Parameter Servers|Parameter Servers]].  Parameter servers, PS vs Hadoop, State Synchronous Parallel (SSP) model, Managed Communication in PS, LDA Sampler with PS
 
* Mon Apr 23, 2018 [[Class meeting for 10-405 Parameter Servers|Parameter Servers]].  Parameter servers, PS vs Hadoop, State Synchronous Parallel (SSP) model, Managed Communication in PS, LDA Sampler with PS
−
** '''Previous assignment due'''
 
 
* Wed Apr 25, 2018 [[Class meeting for 10-405 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
 
* Wed Apr 25, 2018 [[Class meeting for 10-405 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
 +
** '''Last assignment due'''
 
* Mon Apr 30, 2018 [[Class meeting for 10-405 Review session for final|Review session for final]].   
 
* Mon Apr 30, 2018 [[Class meeting for 10-405 Review session for final|Review session for final]].   
 
* Wed May 2, 2018 [[Class meeting for 10-405 Final Exam|Final Exam]].
 
* Wed May 2, 2018 [[Class meeting for 10-405 Final Exam|Final Exam]].

Revision as of 17:31, 19 March 2018

This is the syllabus for Machine Learning with Large Datasets 10-405 in Spring 2018.

Ideas for open-ended extensions to the HW assignments

This is not a complete list! you can use any of these as a starting point, but feel free to think up your own extensions.

HW2 (NB in GuineaPig):

  • The assignment proposes one particular scheme for parallelizing the training/testing algorithm. Consider another parallelization algorithm.
  • Implement a similarly scalable Rocchio algorithm and compare it with NB.
  • Reimplement the same algorithm in Spark (or some other dataflow language) and compare.
  • One or the extensions to GuineaPig not discussed in class is an in-memory map-reduce system. Design an experiment that makes use of this constructively.

HW3 (Logistic regression and SGD)

  • Evaluate the hash trick for Naive Bayes systematically on a series of datasets.
  • Implement a parameter-mixing version of logistic regression and evaluate it.
  • A recent paper proposes (roughly) using SVM with NB-transformed features. Implement this and compare.
  • The personalization method described in class is based on a transfer learning method which works similarly. Many wikipedia pages are available in multiple languages, and works in related languages tend to be lexically similar (eg, "astrónomo" is Spanish for "astronomer"). Suppose features were character n-grams (eg "astr", "stro", "tron", ...) - does domain transfer work for the task of classifying wikipedia pages? Construct a dataset and experiment to test this hypothesis.

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.

Schedule

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