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Computer Engineering (Academic Year 2018/2019) - Programming and security

Artificial Intelligence



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

Videolesson

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