machine learning: advanced topics overview
TRANSCRIPT
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Sergii Shelpuk
August 2013
Machine Learning
Advanced Topics
Overview
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Agenda
1. Machine Learning: current state and open problems
2. Best conferences and publications in Machine Learning
3. Deep Learning: overview4. Reinforcement Learning: overview5. ICML 2013 overview6. Current state of the art in ML7. Q&A
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Current State and Open Problems
Years State Examples
Before 1960 Initial approach Samuel’s checkers
1960 - 1970 Rise of neural networks Perceptron
1970 - 1980 Fall of neural networks
1980 - 1990 Jeff Hinton popularizes backpropagation. Interest to NN comes back
Convolutional networksLeNET and others
1990 - 2000 Era of large margin classifiers SVM, Kernels
2000 - present Era of neural networks. Jeff Hinton and others pioneered deep learning
Google Image SearchSpeech recognitionFace recognition
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TOP ML Events
Microsoft Academic Research
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Deep Learning - Overview
What features should I put in?
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Deep Learning - Overview
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Deep Learning - Overview
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Deep Learning - Overview
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Deep Learning - Overview
ML Classifier
Data
Feat
ure
ext
ract
or Features
Class
All specialized computer vision/speech recognition/etc algorithmsPrevious state of the arts
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Deep Learning - Overview
See also: Jeff Hawkins, On Intelligence
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Deep Learning - Overview
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Deep Learning - Overview
wheels
steering wheel
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Deep Learning - Overview
Autoencoder
See more: Stanford UFLDL Tutorial
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Deep Learning - Overview
See more: Convolutional Deep Belief Networksfor Scalable Unsupervised Learning of Hierarchical Representations
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Deep Learning - Overview
See more: Building high-level features using large scale unsupervised learning
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Deep Learning - Overview
Pre-trained as AutoencoderTypical classification
neural network
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Deep Learning - Overview
Results: Video Recognition
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Deep Learning - Overview
Results: Phoneme Classification
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Deep Learning - Overview
Other Results
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Deep Learning - OverviewImage-Net Competition
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Deep Learning - OverviewImage-Net CompetitionResults from the testset
See more: ImageNet Classification with Deep Convolutional Neural Networks
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Deep Learning - Overview
Error rates on the ILSVRC-2012 competition
▪ University of Tokyo
▪ Oxford University Computer Vision Group
▪ INRIA (French national research institute in CS) + XRCE (Xerox Research Center Europe)
▪ University of Amsterdam
▪ 26.1% 53.6%
▪ 26.9% 50.0%
▪ 27.0%
▪ 29.5%
classificationclassification&localization
▪ University of Toronto (Alex Krizhevsky) ▪ 16.4% 34.1%▪
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Reinforcement Learning - Overview
System
Control Agent
Observe state of the system
Send control signals
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Reinforcement Learning - Overview
State 1
State 3
State 4
State 5
State 8
State 7
State 6
State 2State 10
State 9
Current state
Undesirable state
Undesirable state
Desirable state
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Reinforcement Learning - Overview
State 1 State 2 State 3 State 4 State 5 … State n
State 1 0.05 0.01 0.02 0.15 0.03 … 0.08
State 2 0.02 0.01 0.15 0.18 0.11 … 0.12
State 3 0.01 0.15 0.19 0.5 0.12 … 0.18
… … … … … … … …
State n 0.8 0.05 0.01 0.01 0.02 … 0.1
For action 1
State 1 State 2 State 3 State 4 State 5 … State n
State 1 0.04 0.02 0.01 0.35 0.01 … 0.08
State 2 0.02 0.01 0.15 0.18 0.11 … 0.12
State 3 0.01 0.15 0.19 0.5 0.12 … 0.18
… … … … … … … …
State n 0.8 0.05 0.01 0.01 0.02 … 0.1
For action 2
…For action n
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Reinforcement Learning - Overview
Inverted pendulum problem
http://www.youtube.com/watch?v=aH_d_d_DTkU
Stanford robotic helicopter
http://www.youtube.com/watch?v=Idn10JBsA3Q
ETH Zurich Quadrocopter Pole Acrobatics
http://www.youtube.com/watch?v=pp89tTDxXuI
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ICML 2013 overview
4 days conference2 days workshopTop universities and researchers
Tracks:• Deep Learning• Compressed Sensing• Reinforcement Learning• Topic Modeling• General SVM and Decision Tree Methods• Spectral Learning & Tensors• Online Learning• Structured Labeling• Dimensionality Reduction• Statistical Methods• Transfer Learning• General Methods• Optimization• Clustering• Learning Theory• Dimensionality Reduction and Semi-Supervised Learning• Computer Vision• Kernel Methods• Matrix Factorization
All materials are available online:http://icml.cc/2013/
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Just few photos :-)
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ICML 2013 Materials
Multiple Clustering:http://dme.rwth-aachen.de/en/DMCS
Maxout networks:http://jmlr.org/proceedings/papers/v28/goodfellow13.pdf
Large scale deep belief networks on GPU:http://jmlr.org/proceedings/papers/v28/coates13.pd
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The End