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

 07/08/2-13: Published the text and the results for the 26/07/2013 exam
 18/07/2-13: Published the text and the results for the 11/07/2013 exam
 13/06/2013: Update to detailed schedule for the extra lecture on 17/06/2013
 05/03/2013: 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.

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:

  • Monday 16:15-18:15 in room A3.8
  • Tuesday 08:15-10:15 in room A3.8

A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (I will notify you by email). Please note that not all Tuesdays and Wednesdays are in!!

Date Day Time Room Teacher Topic
05/03/2013 Tuesday 08:15 - 10:15 3.8 Matteo Matteucci Course introduction: from artificial intelligence to natural computation
11/03/2013 Monday 16:15 - 18:15 4.1 Matteo Matteucci Useful background: Maximum Likelihood Estimation and Gradient descent optimization
12/03/2013 Tuesday 08:15 - 10:15 3.8 Matteo Matteucci Introduction to neaural networks, the biologic neuron, the perceptron
18/03/2013 Monday 16:15 - 18:15 4.1 --- No Lecture
19/03/2013 Tuesday 08:15 - 10:15 3.8 --- No Lecture
25/03/2013 Monday 16:15 - 18:15 4.1 Andrea Bonarini Knowledge representation: introduction
26/03/2013 Tuesday 08:15 - 10:15 3.8 Andrea Bonarini Knowledge representation: the conceptual layer
01/04/2013 Monday 16:15 - 18:15 4.1 --- No Lecture
02/04/2013 Tuesday 08:15 - 10:15 3.8 --- No Lecture
08/04/2013 Monday 16:15 - 18:15 4.1 Matteo Matteucci Hebbian learning, Perceptron learning exercise, the XOR problem
09/04/2013 Tuesday 08:15 - 10:15 3.8 Matteo Matteucci Feed forward neural networks: backpropagation
15/04/2013 Monday 16:15 - 18:15 4.1 Matteo Matteucci Demo and derivation of squared error function
16/04/2013 Tuesday 08:15 - 10:15 3.8 Matteo Matteucci Cross Entropy error function, Overfitting, Crossvalidation techniques, Early stopping
22/04/2013 Monday 16:15 - 18:15 4.1 --- No Lecture because of Lauree
23/04/2013 Tuesday 08:15 - 10:15 3.8 Matteo Matteucci Early Stopping and Weight Decay
29/04/2013 Monday 16:15 - 18:15 4.1 Matteo Matteucci Recurrent Architectures
30/04/2013 Tuesday 08:15 - 10:15 3.8 Matteo Matteucci Radial Basis Functions
06/05/2013 Monday 16:15 - 18:15 4.1 --- No Lecture because of Lauree
07/05/2013 Tuesday 08:15 - 10:15 3.8 --- No Lecture because of Lauree
13/05/2013 Monday 16:15 - 18:15 4.1 Andrea Bonarini KR: the technique layer
14/05/2013 Tuesday 08:15 - 10:15 3.8 Andrea Bonarini KR: knowledge modeling
20/05/2013 Monday 16:15 - 18:15 4.1 Andrea Bonarini Expert systems
21/05/2013 Tuesday 08:15 - 10:15 3.8 Andrea Bonarini Knowledge acquisition
27/05/2013 Monday 16:15 - 18:15 4.1 Matteo Matteucci Introduction to Genetic Algoritmhs
28/05/2013 Tuesday 08:15 - 10:15 3.8 Matteo Matteucci Genetic Algorithms: conding and operators (I)
03/06/2013 Monday 16:15 - 18:15 4.1 Matteo Matteucci Genetic Algorithms: conding and operators (II)
04/06/2013 Tuesday 08:15 - 10:15 3.8 Matteo Matteucci Estimation of distribution algorithms
10/06/2013 Monday 16:15 - 18:15 4.1 Andrea Bonarini Uncertainty representation
11/06/2013 Tuesday 08:15 - 10:15 3.8 Andrea Bonarini Fuzzy sets
17/06/2013 Monday 10:15 - 12:15 4.2 Matteo Matteucci Questions and Answers
17/06/2013 Monday 16:15 - 18:15 4.1 Andrea Bonarini Fuzzy rules
18/06/2013 Tuesday 08:15 - 10:15 3.8 Andrea Bonarini Fuzzy systems

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.

As a reference for the exam format you can have a look at the following material from KE:

or from the Italian version (Ingegneria della Conoscenza):

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

Knowledge Engineering

Uncertainty management and Fuzzy systems

Additional Lectures to complement Slides and Textbooks

  • Appunti (bigino): these are notes taken in the past by some students of mine ... they are in Italian!