carsten rother, dmitrij schlesinger
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WS2015/2016, 09.12.2015
Intelligent Systems
Introduction to Machine Learning
Carsten Rother, Dmitrij Schlesinger
09.12.2015Intelligent Systems: Introduction 2
Machine Learning everywhere
Recommender Systems
Slide credits: Bernt Schiele, Stefan Roth, Peter Gehler …
09.12.2015Intelligent Systems: Introduction 3
Machine Learning everywhere
AdWords predictions
09.12.2015Intelligent Systems: Introduction 4
Machine Learning everywhere
Ranking web queries
09.12.2015Intelligent Systems: Introduction 5
Machine Learning everywhere
Image segmentation
09.12.2015Intelligent Systems: Introduction 6
Machine Learning everywhere
Body part detection on Kinect
09.12.2015Intelligent Systems: Introduction 7
What is Machine Learning
Semantics and Structure
Database of Text
Images and Videos
Symbolic Data and Measurements
2D, 3D microscopy
09.12.2015Intelligent Systems: Introduction 8
What is Machine Learning
Goal of machine learning:
Machines that learn to perform a task from experience
We can formalize this as:
is called output variable,
the input variable and
the model parameters
learn... adjust the parameters
… to perform …
... a task ... the function
... from experience using a training dataset , where of either
or
09.12.2015Intelligent Systems: Introduction
Performance: “99% correct classification”
- of what ?
- i.e. on speech recognition (correct words, characters, speakers
identification etc.)
- over which dataset ?
- remember on “precision/recall” from the previous lectures
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What does it mean “to perform” ?
“The car drives without human intervention 99% of
the time on country roads”
Is 99% good enough ?
1% false alarm for 300.000 passengers at airport Frankfurt ?
09.12.2015Intelligent Systems: Introduction
• Supervised learning: there is a completely labelled (annotated) dataset
• Unsupervised learning: no annotations at all
• Reinforcement learning: there is a reward for correct action/recognition
Some variants:
• Semi-Supervised learning: dataset is partially labelled
• Transductive learning: no model, just find the “true” answer
• On-line learning: the training samples are not available at a time
• Large-scale learning: lots of data
• Active learning: how to ask a “teacher” for an annotation ?
10
Different learning scenarios
09.12.2015Intelligent Systems: Introduction
Training:
training data
Testing:
Testing data (different from training)
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General Paradigm (supervised learning)
Unknown parameter
Learn
Predict0,1,2 …
prediction
09.12.2015Intelligent Systems: Introduction
Assume, it is necessary to place a (predefined) number of radio
stations in order to supply the signal of the best quality.
We should:
• Assign each house to a station
• find the best station positions (in order to minimize e.g. the total
distance)
If the assignments are known, it would be a supervised learning. Now
however, we have to find both classification (house assignments) and
unknown parameters (station positions) simultaneously.
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Unsupervised learning
09.12.2015Intelligent Systems: Introduction
Given a situation find an action to maximize a reward function
No optimal output action is presented
Each action yields a reward (which may have to be approximated)
Exploration: try out new actions
Exploitation: use known actions that yield high rewards
Find a good trade-off between exploration and exploitation
Examples: learning robot motor primitives: ball in a cup, elevator
scheduling, Playing Backgammon/Go …
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Reinforcement Learning
09.12.2015Intelligent Systems: Introduction 14
Going back to 1973• Sir James Lighthill report to the British Parliament
Full report on Youtube: http://www.youtube.com/watch?v=FLnqHzpLPws&list=PL27303EC6EC90FD5A
09.12.2015Intelligent Systems: Introduction 15
Going back to 1973• Sir James Lighthill report to the British Parliament
He specifically mentioned the problem of "combinatorial explosion" or "intractability",
which implied that many of AI's most successful algorithms would grind to a halt on
real world problems and were only suitable for solving "toy" versions.
09.12.2015Intelligent Systems: Introduction 16
Questions in Machine Learning
• What is the modelling language:
undirected / directed Graphical models; unstructured models, …
• How does the model look like:
What is the structure?
How do the functions look like?
• Can we learn the Model from Data:
Learn structure
Learn potential functions
• Probabilistic Learning / Discrimantive Learning
• How do we optimize the model (perform inference):
fast, approximate
• Exactly solvable?
09.12.2015Intelligent Systems: Introduction 17
What is Machine Learning
Goal of machine learning:
Machines that learn to perform a task from experience
We can formalize this as:
is called output variable,
the input variable and
the model parameters
learn... adjust the parameters
… to perform …
... a task ... the function consider „probabilistic models“
... from experience using a training dataset , where of either
or
09.12.2015Intelligent Systems: Introduction
09.12: Introduction to Machine Learning, Probability Theory
16.12: Decision Making, Statistical Learning (Schlesinger)
06.01: Directed Graphical Models (Rother)
13.01: Undirected Graphical Models (Rother)
20.01: Neural Networks (Schlesinger)
27.01: Reinforcement Learning (Rother)
02.02: Wrap-up (Rother)
First exercise: 14.12. about Probability Theory (Heidrich)
Course homepage: http://cvlab-dresden.de/courses/intelligente-systeme/
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Lecture overview
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