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 utility-based 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:
breadth-first, depth-first, uniform-cost, iterative-deepening 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.
Hill-climbing, 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 |
Knowledge-based 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.
Inference-based procedure and model-based procedure for propositional (un)satisfiability.
Conjunctive normal form.
The resolution rule for CNF knowledge bases.
Examples of inferences.
A sound, complete and terminating resolution-based 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 first-order logic (FOL).
Pros and cons of propositional logic (PL).
Extending PL to FOL.
Syntax and semantics of FOL.
Entailment, validity and satisfiability.
|
First-order Logic |
Mar 22 |
Midterm |
All of the above |
Mar 27 Mar 29 |
Quantifiers and their use.
Equality.
Using first-order logic to model the world.
Formalizing English statements in FOL.
Typed vs. untyped versions of FOL.
Examples and exercises.
Knowledge engineering in FOL.
Logic-based agents.
Example: the Wumpus world.
|
First-order 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 feed-forward networks.
Perceptrons.
The perception learning algorithm.
Properties.
|
Neural Networks |
May 7 |
Final Exam |
All of the above |