Difference between revisions of "SC:Knowledge Engineering"

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(Created page with '__FORCETOC__ The following are last minute news you should be aware of ;-) 09/03/2010: The course starts today! ==Course Aim & Organization== The objective of this course is ...')
 
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==Course Aim & Organization==
 
==Course Aim & Organization==
  
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The objective of this course is to give an advanced presentation of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling.
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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===
 
===Teachers===
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===Course Program and Schedule===
 
===Course Program and Schedule===
  
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These techniques are presented from a theoretical (i.e., statistics and information theory) and practical perspective through the descriptions of algorithms, their implementation, and applications.The course is composed by a set of selfcontained lectures on specific techniques such as decision trees, decision rules, Bayesian networks, clustering, etc. Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is:
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Lectures will cover the following topics:
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* Artificial intelligence: problems, approaches and applications
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* Models for knowledge representation biologically inspired and adaptive models.
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* Machine Learning: supervised methods, unsupervised methods, reinforcement learning.
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* Artificial Neural Networks and Genetic Algorithms.
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* Knowledge representation techniques: semantic networks, frames, objects, production rules.
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* Uncertainty and approximate knowledge representation. Fuzzy systems.
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* Knowledge engineering: methods to develop knowledge based systems.
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* Knowledge acquisition, conceptualization and modeling.
  
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* '''''Machine Learning and Pattern Classification''''': in this part of the course the general concepts of Machine Learning and Patter Recognition are introduced with a brief review of statistics and information theory;
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Lectures are on:
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* '''''Unsupervised Learning Techniques''''': the most common approaches to unsupervised learning are described mostly focusing on clustering techniques, rule induction, Bayesian networks and density estimators using mixure models;
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* Tuesday 8:15-10:15 in room A3.6
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* '''''Supervised Learning Techniques''''': in this part of the course the most common techniques for Supervised Learning are described: decision trees, decision rules, Bayesian classifiers, hidden markov models, lazy learners, etc.
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* Wednesday 10:15-12:15 in room A3.6
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* '''''Feature Selection and Reduction''''': techniques for data rediction and feature selection will be presented with theory and applications
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* '''''Model Validation and Selection''''': model validation and selection are orthogonal issues to previous technique; during the course the fundamentals are described and discussed (e.g., AIC, BIC, cross-validation, etc. ).
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A detailed schedule of the course can be found here; topics are just indicative while days and teachers are corret "up to some last minute change".
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===Course Evaluation===
 
===Course Evaluation===
  
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The course evaluation is composed by two parts:
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The course evaluation is performed by a written exam divided in two parts:
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# Topics covered by the course teacher during the course
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* A homework with exercises covering the whole program that counts for 30% of the course grade
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# Topics covered by the teaching assistant during the course
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* A oral examination covering the whole progran that count for 70% of the course grade
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Each part will score 16/32 and the exam is passed if the sum of the two is at least 18 after rounding.
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The homework is just one per year, it will be published at the end of the course and you will have 15 days to turn it in. It is not mandatory, however if you do not turn it in you loose 30% of the course grade. There is the option of substitute the homework with a practical project, but this has to be discussed and agreed with the course professor.
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==Teaching Material==
 
==Teaching Material==

Revision as of 01:33, 5 January 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.

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:

  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 is at least 18 after rounding.

Teaching Material

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

Machine Learning and Pattern Recognition

Clustering

Dimensionality Reduction and Feature Selection

Homeworks

The homework, although not mandatory, counts for the 30% of the course grade (i.e., if you do not turn it in you loose 30% of the final grade). You have 15 days to turn it in to the teacher. This year the homework is due by the 3rd of July!

Past years course homework; you can use them to make some practice and prepare this year homework ;-)

Additional Lecture Notes and Bibliography

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