2. 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
experts🡪failure of expert systems in the 1980’s.
Alpydin & Ch. Eick: ML Topic1
2
11. 11
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
12. 12
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.
13. 13
Face Recognition
Training examples of a person
Test images
AT&T Laboratories, Cambridge UK
http://www.uk.research.att.com/facedatabase.html
16. 16
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
17. 17
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
18. 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 ( Bread | Milk ) = 0.6
Market-Basket transactions
19. 19
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, ...
20. Reinforcement learning
■ Reinforcement learning is also based on feedback
provided by the environment. However, in this case, the
information is more qualitative and doesn't help the
agent in determining a precise measure of its error.
■ this feedback is usually called reward (sometimes, a
negative one is defined as a penalty) and it's useful to
understand whether a certain action performed in a state
is positive or not.
20
