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Giovanni Felici
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- Objectives
- What is AI
- Foundations of AI
- History of AI
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Giovanni Felici
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- Agents and environments
- The nature of environments
- The structure of agents
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Giovanni Felici
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- Example problems
- Tree search and graph search
- Uninformed search
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Giovanni Felici
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- Greedy search
- A* search
- Heuristic functions
- Local search
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Giovanni Felici
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- Definition of CSP
- Constraint propagation
- Search in CSP
- Structure of CSP
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Giovanni Felici
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- Logical agents
- Logic, formally
- Propositional Logic
- Theorem proving
- Special CNF systems
- Satisfiability
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Giovanni Felici
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- Semantic & syntax
- Quantifiers
- Numbers, sets, lists
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Giovanni Felici
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- Reducing to propositional inference
- Unification
- Forward chaining
- Backward chaining
- Resolution
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Giovanni Felici
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- Definitions
- Complexity of planning
- Algorithms for planning
- Heuristics for planning
- The planning graph
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Giovanni Felici
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- Planning and scheduling
- Critical path method
- Hierarchical planning
- Planning in other domains
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Giovanni Felici
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- Uncertainty
- Probability
- Inference
- Bayes’ theorem
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Giovanni Felici
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- Introduction to Bayesian Networks
- Conditional independence in BN
- Exact inference
- Approximated inference
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Giovanni Felici
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- Time and uncertainty
- Four tasks of temporal models
- Hidden Markov Models
- Kalman Filters
- Dynamic Bayesian Networks
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Giovanni Felici
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- Utility theory
- Decision networks
- The value of information
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Giovanni Felici
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- Sequential decision problems
- The Bellman equation
- Partially observable Markov decision processes
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Giovanni Felici
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- Decisions with multiple agents
- Dominance and equilibrium
- Mechanism design and auctions
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Giovanni Felici
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- Forms of learning
- Supervised learning
- Decision Trees
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Giovanni Felici
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- Linear regression
- Linear classification
- Logistic regression
- Neural Networks
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Giovanni Felici
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- Support Vector Machines
- Non parametric models
- Nearest Neighbor
- Non parametric regression
- Ensamble learning
- Computational learning theory
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Giovanni Felici
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- Knowledge in learning
- Learning with background
- Statistical learning with complete knowledge
- Statistical learning with uncomplete knowledge
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