Amit Sethi, EEE, IIT G @ Cepstrum, Oct 16, 2011
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Objectives:
 Understand what is machine learning
 Motivate why it has become so important
 Identify Types of learning and salient frameworks, algorithms and their utility
 Take a sneak peak at the next set of problems
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 What is learning?
 Why learn?
 Types of learning and salient frameworks
 Frontiers
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 Example: Learning to ride a bicycle
 T: Task of learning to ride a bicycle
 P: Performance of balancing while moving
 E: Experience of riding in many situations
 Is it wise to memorize all situations and appropriate responses by observing an expert?
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Improve on task, T, with respect to performance metric, P, based on experience, E.
T: Playing checkers
P: Percentage of games won against an arbitrary opponent
E: Playing practice games against itself
T: Recognizing hand-written words
P: Percentage of words correctly classified
E: Database of human-labeled images of handwritten words
T: Driving on four-lane highways using vision sensors
P: Average distance traveled before a human-judged error
E: A sequence of images and steering commands recorded while observing a human driver.
T: Categorize email messages as spam or legitimate.
P: Percentage of email messages correctly classified.
E: Database of emails, some with human-given labels
Source: Introduction to Machine Learning by Raymond J. Mooney
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Determine f such that y n
=f(x n
) and g(y, x) is minimized for unseen values of y and x pairs.
Form of f is fixed, but some parameters can be tuned:
 So, y=f
θ
(x ), where, x is observed, and y needs to be inferred
 e.g. y=1, if mx > c, 0 otherwise, so θ = (m,c)
Machine Learning is concerned with designing algorithms that learn “better” values of θ given “more” x (and y) for a given problem
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What is the scope of the task?
How will performance be measured?
How should learning be approached?
 Scalability:
 How can we learn fast?
 How much resources are needed to learn?
 Generalization:
 How will it perform in unseen situations?
 Online learning:
 Can it learn and improve while performing the task?
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Artificial Intelligence
Data Mining
Probability and Statistics
Information theory
Numerical optimization
Adaptive Control Theory
Neurobiology
Psychology (cognitive, perceptual, dev.)
Linguistics
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 What is learning?
 Why learn?
 Types of learning and salient frameworks
 Frontiers
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Develop systems that are too difficult/expensive to construct manually because they require specific detailed skills or knowledge tuned to a specific task ( knowledge engineering bottleneck ).
Develop systems that can automatically adapt and customize themselves to individual users.
 Personalized news or mail filter
 Personalized tutoring
Discover new knowledge from large databases ( data mining ).
 Market basket analysis (e.g. diapers and beer)
 Medical text mining (e.g. migraines to calcium channel blockers to magnesium)
Source: Introduction to Machine Learning by Raymond J. Mooney
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 Computational studies of learning may help us understand learning in humans and other biological organisms.
 Hebbian neural learning
▪ “Neurons that fire together, wire together.”
 Power law of practice log(# training trials)
Source: Introduction to Machine Learning by Raymond J. Mooney
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 Many basic effective and efficient algorithms available
 Large amounts of data available
 Large amounts of computational resources available
Source: Introduction to Machine Learning by Raymond J. Mooney
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Automatic vehicle navigation
• Road recognition
• Automatic navigation
Speech recognition
• Speech to text
• Automated services over the phone
Face detection
• Facebook face tagging suggestions
• Camera autofocus for portraits
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 What is learning?
 Why learn?
 Types of learning and salient frameworks
 Frontiers
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 Remember, y=f
θ
(x)?
 y can be continuous or categorical
 y may be known for some x or none at all
 f can be simple (e.g. linear) or complex
 f can incorporate some knowledge of how x was generated or be blind to the generation
 etc…
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Supervised learning:
 For, y=f
θ
(x), a set of x i, y i
(usually classes) are known
 Now predict y j
Examples: for new x j
 Two classes of protein with given amino acid sequences
 Labeled male and female face images
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In a nutshell:
 Input is non-linearly transformed by hidden layers usually a “fuzzy” linearly classified combination
 Output is a linear combination of the hidden layer
Use when:
 Want to model a non-linear function
 Labeled data is available
 Don’t want to write new s/w
Variations:
 Competitive learning for classification
 Many more…
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In a nutshell:
 Learns optimal boundary between two classes (red line)
Use when:
 Labeled class data is available
 Want to minimize chance of error in the test case
Variations:
 Non-linear mapping of the input vectors using “Kernels”
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Unsupervised learning:
 For, y=f
θ
(x), only a set of x i are known
 Predict y, such that y is simpler than x but retains its essence
Examples:
 Clustering (when y is a class label)
 Dimensionality reduction
(when y is continuous)
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In a nutshell:
 Grouping a similar objects based on a definition of similarity
 That is, intra vs. inter cluster similarity, e.g. distance from center of the cluster
Use when:
 Class labels are not available, but you have a desired number of clusters in mind
Variations:
 Different similarity measures
 Automatic detection of number of clusters
 Online clustering
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In a nutshell:
 High dimensional data, where not all dimensions are independent, e.g. (x
1
, x
2, x
3
), where x
3
=ax
1
+bx
2
+c
Use when:
 You want to perform linear dimensionality reduction
Variations:
 ICA
 Online PCA
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In a nutshell:
 Learning a lower-dimensional manifold (e.g. surface) close to which the data lies
Use when:
 You want to perform nonlinear dimensionality reduction
Variations:
 SOM
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Generative models:
 For, y=f
θ
(x), we have some idea of how x was generated given x and θ
Examples:
 HMMs: Given phonemes and {age, gender}, we know how the speech can be generated
 Bayesian Networks: Given {gender, age, race} we have some idea of what a face will look like for different emotions
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Discriminative Models:
 Do not care about how the data was generated
 Finding the right features is of prime importance
 Followed by finding the right classifier
Examples:
 SVM
 MLP
Source: “Automatic Recognition of Facial Actions in Spontaneous Expressions” by Bartlett et al in Journal of Multimedia, Sep 2006
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 What is learning?
 Why learn?
 Types of learning and salient frameworks
 Frontiers
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1980s:
 Advanced decision tree and rule learning
 Explanation-based Learning (EBL)
 Learning and planning and problem solving
 Utility problem
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Analogy
 Cognitive architectures
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Resurgence of neural networks (connectionism, backpropagation)
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Valiant’s PAC Learning Theory
 Focus on experimental methodology
1990s
 Data mining
Adaptive software agents and web applications
Text learning
Reinforcement learning (RL)
 Inductive Logic Programming (ILP)
 Ensembles: Bagging, Boosting, and Stacking
 Bayes Net learning
Source: Introduction to Machine Learning by Raymond J. Mooney
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 2000s
 Support vector machines
 Kernel methods
 Graphical models
 Statistical relational learning
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Transfer learning
Sequence labeling
 Collective classification and structured outputs
 Computer Systems Applications
▪ Compilers
▪ Debugging
▪ Graphics
▪ Security (intrusion, virus, and worm detection)
 E mail management
 Personalized assistants that learn
 Learning in robotics and vision
Source: Introduction to Machine Learning by Raymond J. Mooney
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Bioinformatics
• Gene expression prediction (just scratched the surface)
• Automated drug discovery
Speech recognition
• Context recog., e.g. for digital personal assistants (SiRi?)
• Better than Google translate; imagine visiting Brazil
Image and video processing
• Automatic event detection in video
• “Seeing” software for the blind
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Robotics
• Where is my iRobot?
• Would you raise a “robot” child and make it learn?
Advanced scientific calculations
• Weather modeling through prediction
• Vector field or FEM calculation through prediction
Who knows…
• Always in search of new problems
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 Learning the structure of classifiers
 Automatic feature discovery and active learning
 Discovering the limits of learning
 Information theoretic bounds?
 Learning that never ends
 Explaining human learning
 Computer languages with ML primitives
Adapted from: “The Discipline of Machine Learning” by Tom Mitchell, 2006
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Inference: Using a system to get the output variable for a given input variable
Learning: Changing parameters according to an algorithm to improve performance
Training: Using machine learning algorithm to learn function parameters based on input and (optionally) output dataset known as “training set”
Validation and Testing: Using inference (without training) to test the performance of the learned system on data
Offline learning: When all training happens prior to testing, and no learning takes place during testing
Online learning: When learning and testing happen for the same data
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