Human, Animal, and Machine Learning

1. Human, Animal, and Machine Learning Vasile Rus http://www.cs.memphis.edu/~vrus/teaching/cogsci

2. Overview • General Info about this course/seminar • Why Learning? • The ultimate goal: learning to learn • The crux of intelligent systems that enables them to increase adaptivity to environment and therefore chances of survival

3. General Information • Web Page: http://ww.cs.memphis.edu/~vrus/teaching/cogsci/ • Check the page as often as possible • It is the main way of getting latest info

4. General Information • Instructor • Vasile Rus, PhD • Office: 320 Dunn Hall/FIT403c • Phone: x5259 • E-mail: vrus@memphis.edu

5. What is Learning? • the cognitive process of acquiring skill or knowledge (WordNet) • the process of acquiring knowledge, skills, attitudes, or values, through study, experience, or teaching (Wikipedia)

6. Machine Learning? • Processes and algorithms that allow computers to "learn“, i.e. improve their knowledge and performance from experience • applications to • search engines • medical diagnosis • bioinformatics and cheminformatics • detecting credit card fraud • and many more: stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion

7. Goals of this Course • Learn about theory and practice of learning in humans, animals, and machines: • What are the major issues? • What are the major solutions? • How well do they work ? • How do they work ?

8. Goals of this Course • At the end you should: • Agree that learning is subtle and interesting! • Be able to model a problem as a machine learning problem, run machine learning algorithms on some data to solve the problem

9. Questions the Course Will Answer • How people learn? • What are the primitive and patterns of learning? • How animals learn? • Can we make machines learn?

10. Today • Motivation • Course Goals • The Syllabus

11. Syllabus – Student Session • Week 1: Introduction to Machine Learning • Week 2: The WEKA Machine Learning environment • Week 3: Concept Learning • Week 4: Decision Trees Learning • Week 5: Linear Regression and Perceptrons • Week 6: Hypotheses Spaces and Evaluating Hypotheses • Week 7: Graphical Models: Naïve Bayes, Bayes Nets • Week 8: SPRING BREAK

12. Syllabus (cont’d) • Week 9: Graphical Models: Hidden Markov Models • Week 10: Graphical Models: LDA • Week 11: Computational Learning Theory • Week 13: Support Vector Machines • Week 14: Instance-based Learning • Week 15: Project Presentations • Week 16: Project Presentations

13. Syllabus – Plenary Session • Jan 22: NO TALK • Jan 29 • Art Graesser, The University of Memphis: “How Are Theoretical Principles of Learning Incorporated in Intelligent Pedagogical Agents?” • Feb 5 • Razvan Bunescu, Ohio University: “Machine learning approaches to word sense disambiguation and (co)reference resolution” • Feb 12 • Andrew Olney, The University of Memphis: “Building a BrainTrust” • Feb 19 • Giuseppe diFabbrizio, Amazon, Inc.: “Learning to interact - A machine learning approach to dialog management” • Feb 26 • Kim Oller, The University of Memphis: “Evolutionary-Developmental Biology (Evo-Devo) as an influence on current thinking about human and animal language development and evolution” • March 5: NO TALK • Marc 12 • SPRING BREAK

14. Syllabus – Plenary Session • March 19 • Panayiota Kendeou, University of Minnesota: “The Knowledge Revision Components (KReC) framework: We cannot escape the past but we can reduce its impact” • March 26 • Nobal Niraula, The University of Memphis: “A Machine Learning Approach to Anaphora Resolution in Dialogue based Intelligent Tutoring Systems” • April 2 • Phil Pavlik, The University of Memphis: “Results of Data Mining Student Vocabulary Learning” • April 9 • Dan Stefanescu, The University of Memphis: “Short Text Similarity based on Parsing and Information Content” • April 16 • Michael Johnson, Marquette University: Title Pending • April 23 NO TALK • April 30 NO TALK

15. To be successful you need to • Read the syllabus • Understand the structure of the seminar • Read the general policies • Attend sessions and participate by asking questions or/and contributing with related remarks • Explore the seminar website • Don't limit yourself to what is asked in the seminar

16. Grading • Assignments • 4-5 (or more) • 35% of final grade • Project • 40% • Quizzes: 20% • Participation and Presentation: 5%

17. Grading 2.5 above or below the cut-off will earn you a + or – in front of your grade. For example: 89 has a letter equivalent of B+ Exception: 90-91 will give you A-, 92 to 96 will give you A, anything above 97 means A+.

18. Other Issues • Attendance can help you when on borderline • General announcements are posted on the web site frequently! • Please check it out as often as possible • If you notice any inconsistencies on the website (broken links, misspellings, etc.) please notify me • Thank you!

19. Bibliography • Tom Mitchell: Machine Learning, McGraw Hill, 1997, ISBN 0070428077. • RECOMMENDED • Ian H. Witten and Eibe Frank: Data Mining:Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2005, ISBN 0-12-088407-0. • Any graduate textbook on learning

20. Office Hours and Extra Help • During the following times I'll be available in my office • Mondays: 11:00AM-12:00PM • Wednesdays: 1:20-2:20 PM • By appointment • You must send me an email to set up an appointment • If you just knock on my door without notice the chances are that I'll be busy • Please use the office hours!

21. Assignment Submission • Submissions: • You will have on average one-two weeks from the date the work is assigned • Late submissions are not accepted • In exceptional cases you may have a 48-hour grace period at the cost of 50% of the grade (you should ask for it before the due date) • Should be submitted electronically AND on paper

22. Plagiarism • Plagiarism is not tolerated. If caught, you'll be given grade 0 (zero) and disciplinary actions will be taken • It's OK to help some of your friends who may have problems • This is actually a good learning tool • but it is not OK to share answers. If they need, help/discuss with them but never show them your solution • I may (and I will) ask you to demonstrate and explain your solutions

23. Project • Preferably an interdisciplinary team • A common project OR • Something of your choice

24. Machine Learning • The study of automated processes, algorithms, and systems that learn from experience: improve their knowledge and performance

25. A Typical Learning Task • Learning the Sound ‘R’

26. Learning Sound ‘R’ • at least 32 different R sounds to consider as separate distinct sounds • http://mommyspeechtherapy.com/?p=1116 • Vocalic R: • R can be vowel-like too • depending on the location of the R relative to a vowel, the R will change pronunciation • In words like car, fear, for, the R sound comes after the vowels; each vowel is pronounced differently and so is the R • the R takes on the characteristic of the vowel depending on context and combination. The six different vocalic combinations, [ar, air, ear, er, or, ire], are collectively called vocalic R, r-controlled vowels, or vowel R • How do children learn to pronounce R in English?

27. Machine Learning • Standard process but no standard algorithm • Machine Learning task • Learning from experience E with respect to some class of tasks T and performance measure P • Learning is successful if P increases after learning from E

28. Checkers Playing • Task T: play checkers • Performance P: percent of games won against opponents • Training experience E: playing practice games against itself

29. Examples of Learning Tasks • Recognize spoken words • Sphinx system (Lee, 1989) learns speaker specific strategies for recognizing phonemes and words • Neural networks • Hidden Markov models • Learning to drive an autonomous vehicle • Many methods

30. Examples of Learning Tasks • Playing backgammon • Very competitive • Reinforcement learning

31. ML Process • Specify: T, P, E • Specify: the type of knowledge to be learned • Specify: Representation of the target knowledge • Specify: Learning mechanism/algorithm

32. Checkers: ML Process – Step #1 • Task T: learn how to play checkers • Performance P: #wins in world tournament • Experience E: see next slide

33. Checkers: ML Process – Step #1 • Step #1: Choose training experience • Type of feedback: direct or indirect • If indirect feedback is available then there is an issue of credit assignment • In our checkers playing problem, we only know the final result • Control over training experience • Does the learner control the training examples or does a teacher provide them? • Distribution of training examples • Ideally, training examples should have same distribution as testing, future examples

34. Checkers: ML Process – Step #2 • Choose type of knowledge • Target function • Target function: • Find a best search strategy in the space of legal moves that yields best move sequences • ChooseMove : B → M, maps a legal state of the board to a move • V : B → R, maps a legal board state to a real value or score (higher values mean better states)

35. Checkers: ML Process – Step #2 • Value of target function V(b) for a board state b is • V(b)=100, if a final board state is a win • V(b)=-100, if a final board state is a loss • V(b)=0, if a final board state is drawn • V(b)=V(b’) where b’ is the best final board state starting at b and playing optimally • Even V(b) is hard to compute and so an operation description of V is needed • Operational description of ideal function V is an approximation of it

36. Checkers: ML Process – Step #2

37. Checkers: ML Process – Step #3

38. Checkers: ML Process – Step #3

39. Learning Task Elements (so far)

40. Checkers: ML Process – Step #4

41. Checkers: ML Process – Step #4

42. Checkers: ML Process – Step #4 • We need an algorithm for finding weights of a linear function that minimizes E • Incrementally refine weights as new training examples become available • Robust to errors in the training estimated values • LMS algorithm

43. Checkers: ML Process – Step #4

44. 4 Modules of Learning Systems • Performance System • Applies the learned function • Critic • Generates training examples • Generalizer • The learning algorithm • Experiment Generator • Generates problem instances/samples

45. Design Choices

46. Key Issues in ML • What algorithms can learn functions from examples and how well can they do it? • Which algorithms perform well for what types of problems and representations? • How does noise impact learning? • How much training data is sufficient? • Mantra#2: the more data the better but … more data many times is not possible or is expensive • How can prior knowledge be used in learning?

47. Key Issues in ML • What are the limits of learnability? • Under what conditions is successful learning possible, less possible, or impossible? • Nature of learning problems • Under what conditions is a particular learning algorithm assured of learning successfully? • Nature of learning algorithms • How can learners alter their representations to improve?

48. Gist of ML • Experts do their job and many times it is hard to make them articulate a procedure/function that captures their expertise • Basic idea: • Have a mechanism that can learn from examples and hope for the best • Improve your knowledge from more examples

49. Summary • Intro to Machine Learning

50. Next Time • Intro to Weka • Classification and clustering