Difference between revisions of "SC:Knowledge Engineering"

Revision as of 14:58, 24 March 2010

The following are last minute news you should be aware of ;-)

09/03/2010: The course starts today!

Course Aim & Organization

The course introduces the general principles of Artificial Intelligence and its applications. Two approaches to model building and knowledge representation will be presented: the traditional one, based on symbolic representation of knowledge (e.g., frames, rules, fuzzy logic, ...), and one inspired to biological models (e.g., neural networks and genetic algorithms).

Teachers

The course is composed by a blending of lectures and exercises by the course teacher and the teaching assistant.

• Matteo Matteucci: the course teacher
• Andrea Bonarini: the teaching assistant

Course Program and Schedule

Lectures will cover the following topics:

• Artificial intelligence: problems, approaches and applications
• Models for knowledge representation biologically inspired and adaptive models.
• Machine Learning: supervised methods, unsupervised methods, reinforcement learning.
• Artificial Neural Networks and Genetic Algorithms.
• Knowledge representation techniques: semantic networks, frames, objects, production rules.
• Uncertainty and approximate knowledge representation. Fuzzy systems.
• Knowledge engineering: methods to develop knowledge based systems.
• Knowledge acquisition, conceptualization and modeling.

Lectures are on:

• Tuesday 8:15-10:15 in room A3.8
• Wednesday 10:15-12:15 in room A3.8

Course Evaluation

The course evaluation is performed by a written exam divided in two parts (lasting usually 2.5 hours in total):

1. Topics covered by the course teacher during the course
2. Topics covered by the teaching assistant during the course

Each part will score 16/32 and the exam is passed if the sum of the two parts, after rounding, is at least 18.

Teaching Material

The textbooks used for the course are:

• Neural Networks and Pattern Recognition, C. Bishop, Oxford University Press, 1995
• Reti neuronali e metodi statistici, S. Ingrassia, and C. Davino. 2002.
• An introduction to gentic algorithms, Melanie Mitchell, MIT Press, 1998.
• ...
• ...

In the following you can find the lecture slides used by the teacher and the teaching assistant during classes. Some additional material that could be used to prepare the oral examination is provided as well.

Neural Networks and Evolutionary Computation

• Machine Learning Lecture Set: Introduction to the Course and to Machine Learning
• Neural Network Lecture Set: Neural Networks lectures and more
• Genetic Algorithms Lecture Set: Evolutionary Computation and Genetic Algorithms

Knowledge Engineering

• Knowledge Representation Lecture Set: Slides about Knowledge Representation (they are in Italian)

Fuzzy Logic

• ...

Additional Lectures to complement Slides and Textbooks

• Neural Networks FAQ: a great, peer-reviewed, repository of information about neural networks. You should know it by heart!
• Overfitting in Neural Networks: a paper describing the early stopping technique and its rationale.
• Proben 1: read this it together with the previous paper to have a clear idea on how early stopping is related to network evaluation.
• Genetic Algorithm Tutorial: a neat tutorial on genetic algorithms by Darrel Whitley
• Genetic Algorithm with applets: a nice website/tutorial about genetic algorithm I've been inspired for my lectures :-)
• Appunti (bigino): these are notes taken in the past by some students of mine ... they are in Italian!