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  Topics  Readings 

Jan 17 
Course introduction and administration.
Overview of course topics.

 Chap. 1
 Class notes 1 
Jan 19 
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. 
 Chap. 2
 Class notes 2 
Jan 24 Jan 26 
Classes of agents, from simple reflex agents to utilitybased agents.

 Chap. 2
 Class notes 2  Scala examples  Intro to Scala 
Jan 31 
More on Scala. Combining recursion and pattern matching. Recursive methods over lists. Classes and objects in Scala. Inheritance. Subtype and parametric polymorphism. Case classes. Using pattern matching and recursion with userdefined case classes. Examples. 
 Scala examples (revised)  Intro to Scala  Scala tutorial 
Feb 2 
Problem solving. Problem solving agents. Problem solving as search. Modeling problems as search problems. Search space and strategies. General search algorithm. 
 Chap. 3
 Class notes 3  Class notes 3a 
Feb 7 Feb 9 
Search strategies.
General assumptions on environments and cost functions.
Uninformed strategies:
breadthfirst, depthfirst, uniformcost, iterativedeepening search.
Completeness, optimality and complexity.
Comparisons.

 Chap. 3
 Class notes 3a (revised)  Class notes 3b 
Feb 14 Feb 16 
Local search procedures and optimization problems.
Hillclimbing, simulated annealing, beam search and so on.
Genetic algorithms.
problem encodings, combination and mutation.
Examples.

 Chap. 4.1,3,4
 Class notes 4 
Feb 28 Mar 2 
Constraint satisfaction problems.
Classical example: map coloring.
Representing problems as CSPs.
Hard and soft (preference) constraints.
Global constraints.
Constraint satisfaction vs. constraint optimization.

 Chap. 6
 Class notes 6 
Feb 28 Mar 2 
Knowledgebased agents. Knowledge and reasoning as symbolic representation and manipulation. Knowledge inference. Examples: the Wumpus world. Logical agents. Introduction to logic. Propositional logic. Syntax and semantics. Properties. Inference systems for propositional logic. 
 Chap. 7
 Class notes 7  Class notes 7a 
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.

 Chap. 7
 Class notes 7a (revised) 
Mar 14 Mar 16 
Spring break 

Mar 21 
Midterm 
All of the above 
Mar 23 
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. 
 Chap. 8
 Class notes 8 
Mar 28 Mar 30 
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. 

Apr 4 
Guest lecture by Andrew Reynolds on quantifier instantiationbased methods for FOL. 
Talk slides
(not required) 
Apr 6 
Agent acting under uncertainty. The qualification problem. Source of uncertainty. Inadequacy of logicbased approaches and the need for probabilistic methods. Basic introduction to Probability Theory. Reasoning with full joint probability distributions. 
 Chap. 13
 Class notes 13 
Apr 11 Apr 13 
More on conditional probability.
Conditional and unconditional independence between random variables.

 Chap. 13, 14
 Class notes 13 (revised)  Class notes 14 
Apr 18 
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.

 Chap. 14
 Class notes 14 (revised) 
Apr 20 
Introduction to machine learning. Learning agents and forms of learning. Learning from examples. Decision trees. Definition, uses and examples. Learning decision trees. 
 Chap. 18.12
 Class notes 18 
Apr 25 
More on decision tree learning.
Information theoretic measures:
Entropy/information gain.
Choosing attributes bases in information gain.
Examples.
Generalization and overfitting.
Extensions of the basic DT learning algorithm.

 Chap. 18.34
 Class notes 18 (revised) 
Apr 27 
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. 
 Chap. 18.7
 Class notes 18a 
May 2 
Representational power of multilayer feedforward networks. Learning in multilayer networks. The backpropagation learning procedure and its interpretation in terms of gradient descent search. 
 Chap. 18.7
 Class notes 18a (revised) 
May 4 
Introduction to statistical learning.
Bayesian learning.
Optimality of Bayesian learning.
Approximations with MAP hypotheses.

 Chap. 20.12
 Russel's notes 
May 10 
Final Exam 
All of the above 