machine learning
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Introduction toIntroduction toMachine Learning AlgorithmsMachine Learning Algorithms
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What is Artificial Intelligence (AI)?What is Artificial Intelligence (AI)?
Design and study of computer programs that behave intelligently.
Designing computer programs to make computers smarter.
Study of how to make computers do things at which, at the moment, people are better.
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Research Areas and ApproachesResearch Areas and Approaches
ArtificialIntelligence
Research
Rationalism (Logical)Empiricism (Statistical)Connectionism (Neural)Evolutionary (Genetic)Biological (Molecular)
Paradigm
Application
Intelligent AgentsInformation RetrievalElectronic CommerceData MiningBioinformaticsNatural Language Proc.Expert Systems
Learning AlgorithmsInference MechanismsKnowledge RepresentationIntelligent System Architecture
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Concept of Machine LearningConcept of Machine Learning
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InformationTheory
ContextContext
ComputerScience
(AI)
CognitiveScience
Statistics
MachineLearning
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Why Machine Learning?Why Machine Learning?
Recent progress in algorithms and theory Growing flood of online data Computational power is available Budding industry
Three niches for machine learning Data mining: using historical data to improve decisions
Medical records --> medical knowledge Software applications we can’t program by hand
Autonomous driving Speech recognition
Self-customizing programs Newsreader that learns user interests
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Learning: DefinitionLearning: Definition
Definition Learning is the improvement of performance in some
environment through the acquisition of knowledge resulting from experience in that environment.
the improvementof behavior
the improvementof behavior
on someperformance task
on someperformance task
through acquisitionof knowledge
through acquisitionof knowledge
based on partial task experience
based on partial task experience
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A Learning Problem: A Learning Problem: EnjoySportEnjoySport
Sky
What is the general concept?
Temp Humid Wind WaterForecast EnjoySports
Sunny Warm Normal Strong Warm Same Yes
Sunny Warm High Strong Warm Same Yes Rainy Cold High Strong Warm Change No
Sunny Warm High Strong Cool Change Yes
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Metaphors and MethodsMetaphors and Methods
Neurobiology
BiologicalEvolution
HeuristicSearch
StatisticalInference
Memory andRetrieval
ConnectionistLearning
Genetic Learning Tree / RuleInduction
Case-BasedLearning
ProbabilisticInduction
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What is the Learning Problem?What is the Learning Problem?
Learning = improving with experience at some task Improve over task T, With respect to performance measure P, Based on experience E.
E.g., Learn to play checkers T: Play checkers P: % of games won in world tournament E: opportunity to play against self
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Machine Learning: TasksMachine Learning: Tasks
Supervised Learning Estimate an unknown mapping from known input- output pairs Learn fw from training set D={(x,y)} s.t.
Classification: y is discrete Regression: y is continuous
Unsupervised Learning Only input values are provided Learn fw from D={(x)} s.t.
Compression Clustering
Reinforcement Learning
)()( xxw fyf
xxw )(f
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Machine Learning: StrategiesMachine Learning: Strategies
Rote learning Concept learning Learning from examples Learning by instruction Inductive learning Deductive learning Explanation-based learning (EBL) Learning by analogy Learning by observation
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Supervised LearningSupervised Learning
Given a sequence of input/output pairs of the form <xi, yi>, where xi is a possible input and yi is the output associated with xi.
Learn a function f that accounts for the examples seen so far, f(xi) = yi for all i, and that makes a good guess for the outputs of the inputs that it has not seen.
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Examples of Input-Output PairsExamples of Input-Output Pairs
Task Inputs Outputs
Recognition
Action
Janitor robot
problem
Descriptions of
objects
Classes that the
objects belong to
Actions or predictionsDescriptions of
situations
Descriptions of
offices (floor, prof’s office)
Yes or No (indicating
whether or not the
office contains a
recycling bin)
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Unsupervised LearningUnsupervised Learning
Clustering A clustering algorithm partitions the inputs into a fixed
number of subsets or clusters so that inputs in the same cluster are close to one another.
Discovery learning The objective is to uncover new relations in the data.
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Online and Batch LearningOnline and Batch Learning
Batch methods Process large sets of examples all at once.
Online (incremental) methods Process examples one at a time.
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Machine Learning Algorithms and Machine Learning Algorithms and ApplicationsApplications
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Machine Learning AlgorithmsMachine Learning Algorithms
Neural Learning Multilayer Perceptrons (MLPs) Self-Organizing Maps (SOMs)
Evolutionary Learning Genetic Algorithms
Probabilistic Learning Bayesian Networks (BNs)
Other Machine Learning Methods Decision Trees (DTs)
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Neural Nets for Handwritten Digit Neural Nets for Handwritten Digit RecognitionRecognition
…
Pre-processing
…
…
…
… Input units
Hidden units
Output units0 1 2 3 9
…
Training Test
…
…
…
0 1 2 3 9
?
…
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ALVINN System: ALVINN System: Neural Network Learning to Steer Neural Network Learning to Steer
an Autonomous Vehiclean Autonomous Vehicle
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Learning to Navigate a Vehicle by Learning to Navigate a Vehicle by Observing an Human Expert (1/2)Observing an Human Expert (1/2)Inputs
The images produces by a camera mounted on the vehicle
Outputs The actions taken by the human driver to steer
the vehicle or adjust its speed.
Result of learning A function mapping images to control actions
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Learning to Navigate a Vehicle by Learning to Navigate a Vehicle by Observing an Human Expert (2/2)Observing an Human Expert (2/2)
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Data Recorrection by a Hopfield NetData Recorrection by a Hopfield Networkwork
original target data
corrupted input data
Recorrected data after
10 iterations
Recorrected data after
20 iterations
Fullyrecorrected data after
35 iterations
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ANN for Face Recognition
960 x 3 x 4 network is trained on gray-level images of faces to predict whether a person is looking to their left, right, ahead, or up.
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Data MiningData Mining
-- -- ---- -- ---- -- --
-- -- ---- -- ---- -- --
Target data
Cleaned data
Transformed data
Patterns/ model
KnowledgeDatabase/data warehouse
Selection& Sampling
Selection& Sampling
Preprocessing& Cleaning
Preprocessing& Cleaning
Transformation& reduction
Transformation& reduction
Interpretation/Evaluation
Interpretation/EvaluationData MiningData Mining
Performance system
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Hot Water Flashing Nozzle with Hot Water Flashing Nozzle with Evolutionary AlgorithmsEvolutionary Algorithms
Start
Hot water entering Steam and droplet at exit
At throat: Mach 1 and onset of flashing
Hans-Paul Schwefel performed the original experiments
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Machine Learning Applications in Machine Learning Applications in BioinformaticsBioinformatics
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Bayesian NetworksBayesian Networksfor Gene Expression Analysisfor Gene Expression Analysis
Processed
dataData
Preprocessing
Learningalgorithm
Gene C Gene B
Gene A
Target
Gene D
Gene C Gene B
Gene A
Target
Gene D
Gene C Gene B
Gene A
Target
Gene D
Gene C Gene B
Gene A
Target
Gene D
The values of Gene C and Gene B are given.
Belief propagation Probability for the target is computed.
Learning
Inference
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Multilayer Perceptrons for Gene Multilayer Perceptrons for Gene Finding and PredictionFinding and Prediction
Coding potential valueCoding potential value
GC CompositionGC Composition
LengthLength
DonorDonor
AcceptorAcceptor
Intron vocabularyIntron vocabulary
basesDiscrete
exon score
0
1
sequence
score
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Self-Organizing Maps for DNA MiSelf-Organizing Maps for DNA Microarray Data Analysiscroarray Data Analysis
Two-dimensional arrayof postsynaptic neurons
Bundle of synapticconnections
Winning neurons
Input
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Biological Information ExtractionBiological Information ExtractionText Data
DB
LocationDate
DB Record
Database TemplateFilling
Data Analysis &Field Identification
Data Classification &Field Extraction
Information Extraction
Field PropertyIdentification & Learning
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Biomolecular ComputingBiomolecular Computing
011001101010001 ATGCTCGAAGCT