Difference between revisions of "SC:Knowledge Engineering"
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===Course Evaluation=== | ===Course Evaluation=== | ||
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| − | === | + | 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. |
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| + | === Neural Networks and Evolutionary Computation === | ||
* [http://home.dei.polimi.it/matteucc/lectures/MIS/handout-01.pdf Lecture 1]: Introduction to Machine Learning | * [http://home.dei.polimi.it/matteucc/lectures/MIS/handout-01.pdf Lecture 1]: Introduction to Machine Learning | ||
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| − | + | === Knowledge Engineering === | |
| − | + | === Fuzzy Logic === | |
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| − | ===Additional Lecture Notes and | + | === Additional Lecture Notes to complement Slides and Books=== |
| − | * [ | + | * [ftp://ftp.sas.com/pub/neural/FAQ.html Neural Networks FAQ]: a great, peer-reviewed, repository of information about neural networks. You should know it by heart! |
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Revision as of 00:41, 5 January 2010
The following are last minute news you should be aware of ;-)
09/03/2010: The course starts today!
Contents
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 A3.6
- Wednesday 10:15-12:15 in room A3.6
Course Evaluation
The course evaluation is performed by a written exam divided in two parts (lasting usually 2.5 hours in total):
- Topics covered by the course teacher during the course
- 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 is at least 18 after rounding.
Teaching Material
The textbooks used for the course are:
- ...
- ...
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
- Lecture 1: Introduction to Machine Learning
Knowledge Engineering
Fuzzy Logic
Additional Lecture Notes to complement Slides and Books
- Neural Networks FAQ: a great, peer-reviewed, repository of information about neural networks. You should know it by heart!
