Learning from observations

1. Learning from observations Inductive Learning - learning from examples Machine Learning

2. same Environment Environment System Action1 Action2 Knowledge Knowledge changed What Is Machine Learning? “Logic is not the end of wisdom, it is just the beginning” --- Spock time System

3. Learning & Adaptation • ”Modification of a behavioral tendency by expertise.” • (Webster 1984) • ”A learning machine, broadly defined is any device whose • actions are influenced by past experiences.” (Nilsson 1965) • ”Any change in a system that allows it to perform better • the second time on repetition of the same task or on another • task drawn from the same population.” (Simon 1983) • ”An improvement in information processing ability that results • from information processing activity.” (Tanimoto 1990)

4. Ways humans learn things • …talking, walking, running… • Learning by mimicking, reading or being told facts • Tutoring • Being informed when one is correct • Experience • Feedback from the environment • Analogy • Comparing certain features of existing knowledge to new problems • Self-reflection • Thinking things in ones own mind, deduction, discovery

7. A few achievements Programs that can: • Recognize spoken words • Predict recovery rates of pneumonia patients • Detect fraudulent use of credit cards • Drive autonomous vehicles • Play games like backgammon – approaching the human champion!

8. Machine Learning • Machine learning involves automatic procedures that learn a task from a series of examples • Most convenient source of examples is data

9. Learning Definition: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience.

10. Machine Learning Models • Classification • Clustering • Regression • Time series analysis • Association Analysis • Sequence Discovery • ….

11. Text Classification Example Yes No About the Middle East conflict? Text Classification Assign items to one of a set of predefined classes of objects based on a set of observed features Text

12. Text Classification Example Yes No About the Middle East conflict? Text Classification Assign items to one of a set of predefined classes of objects based on a set of observed features

13. Clustering Seeks to place objects into meaningful groups automatically, based on their similarity. Does not require the groups to be predefined. The hope in applying clustering algorithms is that they will discover useful but unknown classes of items.

14. Classification example New data Train set Test set Loan Yes/No Model Learning system

15. Inductive learning • Simplest form: learn a function from examples f is the target function An exampleis a pair (x, f(x)) Problem: find a hypothesish such that h ≈ f given a training set of examples

16. Inductive learning method • Construct/adjust h to agree with f on training set • (h is consistentif it agrees with f on all examples) • E.g., curve fitting:

17. Inductive learning method • Construct/adjust h to agree with f on training set • (h is consistent if it agrees with f on all examples) • E.g., curve fitting:

18. Inductive learning method • Construct/adjust h to agree with f on training set • (h is consistent if it agrees with f on all examples) • E.g., curve fitting:

19. Inductive learning method • Construct/adjust h to agree with f on training set • (h is consistent if it agrees with f on all examples) • E.g., curve fitting:

20. Inductive learning method • Construct/adjust h to agree with f on training set • (h is consistent if it agrees with f on all examples) • E.g., curve fitting:

21. Inductive learning method • Construct/adjust h to agree with f on training set • (h is consistentif it agrees with f on all examples) • E.g., curve fitting: • Ockham’s razor: prefer the simplest hypothesis consistent with data

22. Machine Learning Methods • Instance Based Methods (CBR, k-NN) • Decision Trees • Artificial Neural Networks • Bayesian Networks • Naïve Base • Evolutionary Strategies • Support Vector Machines • ..

23. Classification example Features: height, weight x x o x x o x o x o o Height x o x o o x x o x x o o o x x- weight-lifters o- ballet dancers x Weight

24. Classification example - Simple Model Features: height, weight Decision boundary x x o x x o x o x o o Height x o x o o x x o x x o o o x x- weight-lifters o- ballet dancers x Weight

25. Classification example - Complex model Features: height, weight Complex Decision boundary x x o x x o x o x o o Height x o x o o x x o x x o o o x x- weight-lifters o- ballet dancers x Weight Note: A simple decision boundary is better than a complex one - It GENERALIZES better.

26. Learning Paradigms • Supervised learning - with teacher • inputs and correct outputs are provided by the teacher • Reinforced learning - with reward or punishment • an action is evaluated • Unsupervised learning - with no teacher • no hint about correct output is given

27. Nearest Neighbor • Simple effective approach for supervised learning problems • Envision each example as a point in n-dimensional space - Picture with 2 of them • Classify test point same as nearest training point (Euclidean distance)

28. k-Nearest Neighbor • Nearest Neighbor can be subject to noise • Incorrectly classified training points • Training anomalies • k-Nearest Neighbor • Find k nearest training points (k odd) and vote on which classification • Works on numerical data