SC:Knowledge Engineering
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:
- 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
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
- Lecture 1: Introduction to Machine Learning
- Lecture 2: Probability for Dataminers
- Lecture 3: Decision Trees
- Lecture 4: Decision Rules
- Lecture 5: Bayesian Classifiers
- Lecture 6: Bayesian Networks
- Lecture 7: Markov Chains and Hidden Markov Models
Clustering
- Clustering Lecture 1: Introduction
- Clustering Lecture 2: K-Means and Hierarchical
- Clustering Lecture 3: Fuzzy, Gaussians, and SOM
- Clustering Lecture 4: Vector Spacec Model and PDDP
- Clustering Lecture 5: DBSCAN and Jarvis Patrick
- Clustering Lecture 6: Evaluation measures
Dimensionality Reduction and Feature Selection
- Dimensionality Reduction Lecture 1: Dimensionality reduction Intro, Feature extraction, PCA and LDA
- Dimensionality Reduction Lecture 2: Feature selection
- Genetic Algorithms: a rather comprehensive tutorial
- Algorithm Evaluation: from cross-validation to confidence intervals
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 ;-)
- Homework for the academic year 2007/2008 Part 1 and Part 2
- Homework for the academic year 2006/2007
- Homework for the academic year 2005/2006
Additional Lecture Notes and Bibliography
- Bayesian Networks without tears: a useful introduction to Bayesian Network.
- Fundamental Problems for HMM: a document to introduce Hidden Markov Models and the three fundamental questions about them.
- An exercise on modeling and reasoning with Bayesian Networks.
