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Artificial Intelligence

Prof. Giovanni Felici
(Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti” - CNR (Roma - Italia))

Content language:English

Course description

Mandatory course of second year of the Master Degree in Computer Engineering.

Prerequisites

None.

Objectives

The course has the objective to describe methodologies of the artificial intelligence. In particular, will be discussed the concept of intelligent agent, knowledge representation, inference, deduction and machine learning.

Program

The course gives 9 credits and it is composed of 20 hours of video lectures. Video lectures present the following concepts:

Artificial Intelligence. Introduction
Intelligent Agents
Searching
Informed search
Constraints satisfaction problems
Propositional Logic
First Order Logic
Inference in First Order Logic
Planning
Planning in the real world
Quantifying uncertainty
Bayesian Networks
Probabilistic reasoning over time
Making simple decisions
Complex decision making. First part
Complex decision making. Second part
Learning & decision trees
Regression and classification. First part
Regression and classification. Second part
Learning with knowledge & statistical learning

Book

S. J. Russell, P. Norvig, “Artificial Intelligence. A Modern Approach.”, Pearson, Milano
T. Mitchell - "Machine Learning"
S. Haykin, “Neural Networks - A comprehensive foundation”, Prentice-Hall

Exercises

During the lectures there are several exercises to be done at home.

Appointed teaching professor

Professor not available

Video Professor

Prof. Giovanni Felici - Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti” - CNR (Roma - Italia)

List of video lessons

Lesson n. 1: Artificial Intelligence. Introduction Display lessons issues		Giovanni Felici
Objectives What is AI Foundations of AI History of AI
Lesson n. 2: Intelligent Agents Display lessons issues		Giovanni Felici
Agents and environments The nature of environments The structure of agents
Lesson n. 3: Searching Display lessons issues		Giovanni Felici
Example problems Tree search and graph search Uninformed search
Lesson n. 4: Informed search Display lessons issues		Giovanni Felici
Greedy search A* search Heuristic functions Local search
Lesson n. 5: Constraints satisfaction problems Display lessons issues		Giovanni Felici
Definition of CSP Constraint propagation Search in CSP Structure of CSP
Lesson n. 6: Propositional Logic Display lessons issues		Giovanni Felici
Logical agents Logic, formally Propositional Logic Theorem proving Special CNF systems Satisfiability
Lesson n. 7: First Order Logic Display lessons issues		Giovanni Felici
Semantic & syntax Quantifiers Numbers, sets, lists
Lesson n. 8: Inference in First Order Logic Display lessons issues		Giovanni Felici
Reducing to propositional inference Unification Forward chaining Backward chaining Resolution
Lesson n. 9: Planning Display lessons issues		Giovanni Felici
Definitions Complexity of planning Algorithms for planning Heuristics for planning The planning graph
Lesson n. 10: Planning in the real world Display lessons issues		Giovanni Felici
Planning and scheduling Critical path method Hierarchical planning Planning in other domains
Lesson n. 11: Quantifyng uncertainty Display lessons issues		Giovanni Felici
Uncertainty Probability Inference Bayes’ theorem
Lesson n. 12: Bayesian Networks Display lessons issues		Giovanni Felici
Introduction to Bayesian Networks Conditional independence in BN Exact inference Approximated inference
Lesson n. 13: Probabilistic reasoning over time Display lessons issues		Giovanni Felici
Time and uncertainty Four tasks of temporal models Hidden Markov Models Kalman Filters Dynamic Bayesian Networks
Lesson n. 14: Making simple decisions Display lessons issues		Giovanni Felici
Utility theory Decision networks The value of information
Lesson n. 15: Complex decision making. First part Display lessons issues		Giovanni Felici
Sequential decision problems The Bellman equation Partially observable Markov decision processes
Lesson n. 16: Complex decision making. Second part Display lessons issues		Giovanni Felici
Decisions with multiple agents Dominance and equilibrium Mechanism design and auctions
Lesson n. 17: Learning & decision trees Display lessons issues		Giovanni Felici
Forms of learning Supervised learning Decision Trees
Lesson n. 18: Regression and classification. First part Display lessons issues		Giovanni Felici
Linear regression Linear classification Logistic regression Neural Networks
Lesson n. 19: Regression and classification. Second part Display lessons issues		Giovanni Felici
Support Vector Machines Non parametric models Nearest Neighbor Non parametric regression Ensamble learning Computational learning theory
Lesson n. 20: Learning with knowledge & statistical learning Display lessons issues		Giovanni Felici
Knowledge in learning Learning with background Statistical learning with complete knowledge Statistical learning with uncomplete knowledge

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Engineering Area

Artificial Intelligence