A Brief Introduction to Graphical Models and Bayesian Networks
	Alison Cawsey: Databases and Artificial Intelligence 3 - Artificial Intelligence Segment. Ein Einführungskurs in KI.
	Andrew Blais und David Mertz: An introduction to neural networks. - Neural nets may be the future of computing. A good way to understand them is with a puzzle that neural nets can be used to solve. Suppose that you are given 500 characters of code that you know to be C, C++, Java, or Python. Now, construct a program that identifies the code's language. One solution is to construct a neural net that learns to identify these languages. This article discusses the basic features of neural nets and approaches to constructing them so you can apply them in your own coding.
	D. Michie, D.J. Spiegelhalter, C.C. Taylor (eds): Machine Learning, Neural and Statistical Classification. - This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems. It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these discplines.
	Notes for an undergraduate course in Artificial Intelligence
	Intelligent Agents