SEM2A4 Theories of Learning

Dr Manfred Kerber

10 credit module Semester 2

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Aims

Objectives

On completion of this course, the student should:

Prerequisites

Basic knowledge of AI Techniques and Logic (as presented in SEM1A8 and SEM1A6). Some programming knowledge in Pop11 (as presented in SEM1A9) is necessary in order to benefit from the implementation examples.

Teaching Methods

The delivery will be a mixture of conventional lectures and exercise classes with two unassessed class tests. (together 2 hours per week throughout the semester).

Students are expected to spend a sixth of their weekly labour time on this 10 credit course (that makes inclusively presence time at least six and a half hours). In particular students are expected to prepare each lecture by reading the advised material or corresponding related material and to work on the exercises. The exercises are crucial for a sufficient understanding of the material presented in the lecture.

Assessment

The course will be assessed by a 2-hour unseen examination in May.

Recommended Books

Title Author(s) Publisher Comments
Artificial Intelligence - A Modern Approach, Chapters 18, 20, and 21 Stuart Russell & Peter Norvig Prentice Hall 1995 Essential
Artificial Intelligence, 2nd Edition, Chapter 17 Elaine Rich & Kevin Knight McGraw-Hill 1994 Further Reading
Artificial Intelligence, Chapter 9 Ian Pratt Macmillan 1994 Further Reading
Artificial Intelligence, Third edition Patrick Henry Winston Addison-Wesley 1992 Further Reading
Learning and Memory - An Integrated Approach John R. Anderson John Wiley & Sons 1995 Further Reading
Induction - Processes of Inference, Learning, and Discovery, 2nd Edition John H. Holland, Keith J. Holyoak, Richard E. Nisbett & Paul R. Thagard MIT Press 1986 Further Reading
Machine Learning Ryszard S. Michalski, Jaime G. Carbonell & Tom M. Mitchell Springer 1984 Further Reading
Inductive Logic Programming Stephen Muggleton (ed.) Academic Press 1992 Further Reading
An Introduction to Kolmogorov Complexity and Its Applications, 2nd Edition Ming Li & Paul Vitányi Springer 1997 Further Reading


Detailed Syllabus and Relevant Links

The following courses are currently planned, modifications are possible. Slides and Exercises are available in gzipped PostScript files, during the course, spare copies can be found in the School's library, these should be taken first in order not to waste printer resources. The files will be made readable after the corresponding lectures took place. You can directly preview/print the files (locally) by something like:
zcat /bham/ftp/pub/authors/M.Kerber/Teaching/SEM2A4/ex2.ps.gz | ghostview -
zcat /bham/ftp/pub/authors/M.Kerber/Teaching/SEM2A4/ex2.ps.gz | lpr -
zcat /bham/ftp/pub/authors/M.Kerber/Teaching/SEM2A4/l2.ps.gz | ghostview -seascape -
zcat /bham/ftp/pub/authors/M.Kerber/Teaching/SEM2A4/l2.ps.gz | psnup -r -4 | lpr -

Week Content Slides Exercises
1
Basic notions of learning (architecture of a learning agent, performance element, feedback, representation issues), inductive learning, reflexive learning, literature Slides Exercises
2
Decision Tree Learning Slides Exercises
3
Decision Tree Learning (Finding best attributes), Current-best-learning Slides Exercises
4
Least-Commitment Search, Computational Learning Theory Slides Exercises
5
Reinforcement Learning (LMS, ADP, TD learning, passive & active learning) Slides Exercises
6
Q-learning Slides Exercises
7
Knowledge in learning, explanation-based learning Slides Exercises
8
Analogy Slides Exercises
9
Inductive Logic Programming Slides Exercises
10
FOIL, Genetic Algorithms Slides Exercises
11
Program Size Complexity, Outlook Slides -
12
Revision lecture - -


A file with all the slides can be found here

Pop11 Program fragments: Note, most of the program fragments can easily be extended to running programs. This is partly a task in the exercises. Here are some slides on the terminology used in the programs.

Topic Program Fragment Full Program
Reflexive Learningp1.pp1-solution.p
Decision Tree Learningp3.pp3-solution.p
Decision Tree Learning (Restaurant Example)restaurant-example.p
Decision Tree Learning (Bank Example)bank-example.p
LMS learning, 3x4-worldp5.pp5-solution.p
TD learning, 3x4-worldp6.pp6-solution.p
Q-learning, 3x4-worldp7.pp7-solution.p


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Maintained by M.Kerber@cs.bham.ac.uk
School of Computer Science
The University of Birmingham

Last update 22 January 1999