Machine learning involves programming computers to optimize performance using example data or past experience. It is used when human expertise does not exist, humans cannot explain their expertise, solutions change over time, or solutions need to be adapted to particular cases. Learning builds general models from data to approximate real-world examples. There are several types of machine learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has applications in many domains including retail, finance, manufacturing, medicine, web mining, and more.
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Why “Learn”?
Machine learning is programming computers to
optimize a performance criterion using example
data or past experience.
There is no need to “learn” to calculate payroll
Learning is used when:
Human expertise does not exist (navigating on Mars),
Humans are unable to explain their expertise (speech
recognition)
Solution changes in time (routing on a computer network)
Solution needs to be adapted to particular cases (user
biometrics)
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What We Talk About When We
Talk About“Learning”
Learning general models from a data of particular
examples
Data is cheap and abundant (data warehouses, data
marts); knowledge is expensive and scarce.
Example in retail: Customer transactions to
consumer behavior:
People who bought “Da Vinci Code” also bought “The Five
People You Meet in Heaven” (www.amazon.com)
Build a model that is a good and useful
approximation to the data.
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Data Mining/KDD
Retail: Market basket analysis, Customer
relationship management (CRM)
Finance: Credit scoring, fraud detection
Manufacturing: Optimization, troubleshooting
Medicine: Medical diagnosis
Telecommunications: Quality of service
optimization
Bioinformatics: Motifs, alignment
Web mining: Search engines
...
Definition := “KDD is the non-trivial process of
identifying valid, novel, potentially useful, and
ultimately understandable patterns in data” (Fayyad)
Applications:
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What is Machine Learning?
Machine Learning
Study of algorithms that
improve their performance
at some task
with experience
Optimize a performance criterion using example
data or past experience.
Role of Statistics: Inference from a sample
Role of Computer science: Efficient algorithms to
Solve the optimization problem
Representing and evaluating the model for
inference
6. Growth of Machine Learning
Machine learning is preferred approach to
Speech recognition, Natural language processing
Computer vision
Medical outcomes analysis
Robot control
Computational biology
This trend is accelerating
Improved machine learning algorithms
Improved data capture, networking, faster computers
Software too complex to write by hand
New sensors / IO devices
Demand for self-customization to user, environment
It turns out to be difficult to extract knowledge from human
expertsfailure of expert systems in the 1980’s.
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8. Learning Associations
Basket analysis:
P (Y | X ) probability that somebody who buys X also
buys Y where X and Y are products/services.
Example: P ( chips | beer ) = 0.7
Market-Basket transactions
TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper, Coke
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Classification
Example: Credit
scoring
Differentiating
between low-risk
and high-risk
customers from
their income and
savings
Discriminant: IF income > θ1 AND savings > θ2
THEN low-risk ELSE high-risk
Model
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Classification: Applications
Aka Pattern recognition
Face recognition: Pose, lighting, occlusion (glasses,
beard), make-up, hair style
Character recognition: Different handwriting styles.
Speech recognition: Temporal dependency.
Use of a dictionary or the syntax of the language.
Sensor fusion: Combine multiple modalities; eg, visual (lip
image) and acoustic for speech
Medical diagnosis: From symptoms to illnesses
Web Advertizing: Predict if a user clicks on an ad
on the Internet.
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Supervised Learning: Uses
Prediction of future cases: Use the rule to predict
the output for future inputs
Knowledge extraction: The rule is easy to
understand
Compression: The rule is simpler than the data it
explains
Outlier detection: Exceptions that are not covered
by the rule, e.g., fraud
Example: decision trees tools that create rules
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Unsupervised Learning
Learning “what normally happens”
No output
Clustering: Grouping similar instances
Other applications: Summarization, Association
Analysis
Example applications
Customer segmentation in CRM
Image compression: Color quantization
Bioinformatics: Learning motifs
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Reinforcement Learning
Topics:
Policies: what actions should an agent take in a particular
situation
Utility estimation: how good is a state (used by policy)
No supervised output but delayed reward
Credit assignment problem (what was responsible
for the outcome)
Applications:
Game playing
Robot in a maze
Multiple agents, partial observability, ...
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Resources: Journals
Journal of Machine Learning Research www.jmlr.org
Machine Learning
IEEE Transactions on Neural Networks
IEEE Transactions on Pattern Analysis and Machine
Intelligence
Annals of Statistics
Journal of the American Statistical Association
...
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Resources: Conferences
International Conference on Machine Learning (ICML)
European Conference on Machine Learning (ECML)
Neural Information Processing Systems (NIPS)
Computational Learning
International Joint Conference on Artificial Intelligence (IJCAI)
ACM SIGKDD Conference on Knowledge Discovery and Data Mining
(KDD)
IEEE Int. Conf. on Data Mining (ICDM)
20. Summary COSC 6342
Introductory course that covers a wide range of machine
learning techniques—from basic to state-of-the-art.
More theoretical/statistics oriented, compared to other
courses I teach might need continuous work not “to get
lost”.
You will learn about the methods you heard about: Naïve
Bayes’, belief networks, regression, nearest-neighbor (kNN), decision
trees, support vector machines, learning ensembles, over-fitting,
regularization, dimensionality reduction & PCA, error bounds,
parameter estimation, mixture models, comparing models, density
estimation, clustering centering on K-means, EM, and DBSCAN, active
and reinforcement learning.
Covers algorithms, theory and applications
It’s going to be fun and hard work
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21. Which Topics Deserve More Coverage
—if we had more time?
Graphical Models/Belief Networks (just ran out of
time)
More on Adaptive Systems
Learning Theory
More on Clustering and Association
Analysiscovered by Data Mining Course
More on Feature Selection, Feature Creation
More on Prediction
Possibly: More depth coverage of optimization
techniques, neural networks, hidden Markov models,
how to conduct a machine learning experiment,
comparing machine learning algorithms,…
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