SC:Knowledge Engineering

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

 12/03/2012: The course starts today! The schedule has already changed to reduce overlaps

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 8:15-10:15 in room A3.8
  • Tuesday 8:15-10:15 in room A4.1

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
12/03/2012 Monday 08:15 - 10:15 3.8 Matteo Matteucci Course introduction: from artificial intelligence to natural computation
13/03/2012 Tuesday 08:15 - 10:15 4.1 Matteo Matteucci Useful background: Maximum Likelihood Estimation and Gradient descent optimization
19/03/2012 Monday 08:15 - 10:15 3.8 Andrea Bonarini ...
20/03/2012 Tuesday 08:15 - 10:15 4.1 Matteo Matteucci Introduction to neaural networks, the biologic neuron, the perceptron model
26/03/2012 Monday 08:15 - 10:15 3.8 Andrea Bonarini ...
27/03/2012 Tuesday 08:15 - 10:15 4.1 Matteo Matteucci Hebbian learning, Perceptron learning exercise, the XOR problem
02/04/2012 Monday 13:15 - 15:15 3.8 Luigi Malago Linear regression methods (see 19/03/2012)
03/04/2012 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Logistic regression (Ch.4.4)
16/04/2012 Monday 13:15 - 15:15 3.8 Matteo Matteucci Logistic regression (Ch.4.4)
17/04/2012 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Perceptron learning
23/04/2012 Monday 13:15 - 15:15 3.8 Luigi Malago Linear regression methods (see 19/03/2012)
24/04/2012 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Maximum margin classification (Ch. 4.5.2)
07/05/2012 Monday 13:15 - 15:15 3.8 Davide Eynard Clustering I: Introduction and K-Means
08/05/2012 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Kernel Smoothing Methods and Kerned Density Estimation (Ch.6.1, Ch.6.6, Ch.6.9)
14/05/2012 Monday 13:15 - 15:15 3.8 Davide Eynard Clustering II: K-Means Alternatives, Hierarchical, SOM
15/05/2012 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Gaussian Mixture Models (Ch.6.8) and the EM Algorithm (Ch.8.5)
21/05/2012 Monday 13:15 - 15:15 3.8 Davide Eynard Clustering III: Mixture of Gaussians, DBSCAN, Jarvis-Patrick
22/05/2012 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Decision Trees (handout + Ch. 9.2)
28/05/2012 Monday 13:15 - 15:15 3.8 Davide Eynard Clustering IV: Evaluation Measures
29/05/2012 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Classification Rules (handout + Ch. 9.2)
04/06/2012 Monday 13:15 - 15:15 3.8 Matteo Matteucci Rule pruning and Sequential Covering Algorithm (handout + Ch. 9.3)
05/06/2011 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Sequential Covering Algorithm (handout + Ch. 9.3)
11/06/2012 Monday 13:15 - 15:15 3.6 Matteo Matteucci Support Vector Machines (Ch. 12.1, Ch. 12.2, Ch. 12.3.0, Ch. 12.3.1 + SVM paper)
12/06/2011 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Model selection theory (Ch. 7.1, Ch. 7.2, Ch. 7.3 + Bias and Variance notes)
TBD TBD TBD TBD Matteo Matteucci Model selection practice (Ch. 7.4, Ch. 7.5, Ch. 7.6, Ch. 7.7, Ch. 7.10)
TBD TBD TBD TBD Matteo Matteucci Question and Answers

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!
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