This page gives highlights of past lectures and provides lecture notes, reading assignments, and exercises.
Chapters and sections in the readings are from the textbook, unless specified otherwise.
Dates  Lecture synopsis  Notes 

Jan 16 
Course introduction and administration.
Overview of course topics.

Introduction 
Jan 18 
Intelligent agents.
(Ideal) Rational agents.
Performance measure.
Environment, percept sequence, actions,
internal knowledge, autonomy.
Examples of natural and artificial agents.
Agents as mappings.
Environment features.

Intelligent Agents 
Jan 23 
Classes of agents, from simple reflex agents to utilitybased agents.

Intelligent Agents 
Jan 25 
More on F#.
Variable scope ands scoping rules. Function definitions.
Recursive functions.
Pattern matching. Patterns in let expressions and in function definitions. The match construct. Meaning and common uses. Scoping of pattern variables in match.


Jan 30 
Algebraic datatypes (ADT), or discriminated unions, in F#.
Basic uses.
Pattern matching with ADTs.
Functions accessing and manipulating ADTs.
Using ADTs to encode arithmetic expressions.
Simple evaluators of arithmetic expressions.
Parametric types in F#. Motivation and uses.
Parametric algebraic datatypes.
F# Lists. Basic features and examples.
Using pattern matching and recursion to implement functions over lists.


Feb 1 
More on F#.
Association lists. Using lists to implement other data types.
Maps as association lists. Sets as lists with no repeated elements.
Option types. Motivation and uses.
Immutable maps.
Mutable and immutable records. Pattern matching with records.
Mutable variables and imperative code.

Problem Solving 
Feb 6 Feb 8 
Modeling problems as search problems.
Search space and strategies.
General search algorithm.
Search strategies.
General assumptions on environments and cost functions.
Uninformed strategies:
breadthfirst, depthfirst, uniformcost, iterativedeepening search.
Completeness, optimality and complexity.
Comparisons.

Uninformed Search Informed Search 
Feb 13 
Completeness, optimality and complexity of A*.
Comparisons with other strategies.

Informed Search 
Feb 15 
Local search procedures and optimization problems.
Hillclimbing, simulated annealing, beam search and so on.
Genetic algorithms.
problem encodings, combination and mutation.
Examples.

Beyond Classical Search 
Feb 20 Feb 22 
Constraint satisfaction problems.
Classical example: map coloring.
Representing problems as CSPs.
Hard and soft (preference) constraints.
Global constraints.
Constraint satisfaction vs. constraint optimization.

Constraint Satisfaction Problems 
Feb 28 Mar 2 
Knowledgebased agents.
Knowledge and reasoning as symbolic representation and manipulation.
Knowledge inference.
Examples: the Wumpus world.
Logical agents.
Entailment and derivability.
Introduction to logic.
Propositional logic.
Syntax and semantics.
Properties.

Logical Agents
Propositional Logic 
Mar 7 Mar 9 
Sound and complete inference systems for propositional logic.
Inferencebased procedure and modelbased procedure for propositional (un)satisfiability.
Conjunctive normal form.
The resolution rule for CNF knowledge bases.
Examples of inferences.
A sound, complete and terminating resolutionbased procedure for CNF satisfiability.
Horn clauses.
Linear methods for Horn clause problems:
forward and backward propagation.

Propositional Logic
(revised) 
Mar 14 Mar 16 
Spring break 

Mar 20 
Introduction to firstorder logic (FOL).
Pros and cons of propositional logic (PL).
Extending PL to FOL.
Syntax and semantics of FOL.
Entailment, validity and satisfiability.

Firstorder Logic 
Mar 22 
Midterm 
All of the above 
Mar 27 Mar 29 
Quantifiers and their use.
Equality.
Using firstorder logic to model the world.
Formalizing English statements in FOL.
Typed vs. untyped versions of FOL.
Examples and exercises.
Knowledge engineering in FOL.
Logicbased agents.
Example: the Wumpus world.

Firstorder Logic 
Apr 3 Apr 5 
Notes on the midterm.

Uncertainty (revised)
Probabilistic Reasoning 
Apr 10 Apr 12 
Computing various conditional and unconditional probabilities from belief networks.
Examples and exercises.
Efficient representation of conditional distribution with Bayesian networks.
Query and inference in Bayesian networks.
Exact inference methods.
The variable elimination algorithm. Clustering algorithms.
Examples.
Complexity of exact reasoning.

Probabilistic Reasoning (revised) 
Apr 17 
More on direct sampling methods.
Likelihood weighting and Markov chain Montecarlo.

Probabilistic Reasoning (revised)
Learning 
Apr 24 Apr 26 
Artificial neural networks.
Motivation and uses.
Units, links, weights and activation functions.
Examples.
Neural network topologies.
Multilayer feedforward networks.
Perceptrons.
The perception learning algorithm.
Properties.

Neural Networks 
May 7 
Final Exam 
All of the above 