Table of Contents
- Schedule and Readings
- Lecture Details
- The course lecturer is Mike Ashcroft:
- Email: email@example.com
- Phone: 0769 415 418
- NB Use the given email address for contact. Do not use Mike's Uppsala University address.
- Administration questions should be directed to Roland Bol:
- Email: Roland.Bol@it.uu.se
- Phone: 018 471 7606.
- Room: 1356
- Guest lectures will be given by Olle Gällmo and Ivan Jordanov.
- The course will be given in English. Assignments must be handed in in English.
2. Schedule and Readings
- Readings refer to chapters (or, in the case of '4.1' chapter and section) in Russell and Norvig, Artificial intelligence : A Modern Approach, 3rd Ed.
- Reading outside brackets are compulsory. Readings in brackets are suggested, and tend to be entire chapters whereas the compulsory readings are selected sections. If you only read the compulsory sections, you may need to look up particular concepts outside these sections.
- The topic schedule is an estimation only. We will proceed as the topics require.
- The assignments are given for the lecture at which work on them will begin.
- The schedule may change depending on lecturer availability.
|36||Fre 7||15:15||Pol_1311||Search 1||Mike||3,4.1 Lecture notes|
|37||Tis 11||13:15||Pol_2347||Natural Computation 1||Olle||Slides|
|37||Tor 13||10:15||Pol_1311||Natural Computation 2||Ivan||Neural network slides Genetic algorithms slides (18.7)|
|37||Fre 14||15:15||Pol_1311||Search 2||Mike||4.3 Lecture notes||1. Planning|
|38||Mån 17||15:15||Pol_1211||Propositional and Predicate Logic||Mike||7.3-7.5,8.1-8.2,9.1-9.5 (7,8,9) Lecture slides|
|38||Ons 19||08:15||Pol_1311||Planning||Mike||10.1-10.3,10.4.4,11.1 (10,11) Lecture slides|
|39||Mån 24||10:15||Pol_1111||Probability Theory||Mike||13.2-13.5 (13) Probability Theory and Bayesian Network Notes|
|39||Ons 26||13:15||Pol_2146||Probabilistic Networks||Mike||14.1-14.5,15.3-15.5 (14,15) Probability Theory and Bayesian Network Notes Related Article|
|39||Fre 28||15:15||Pol_1311||Decision Theoretic Networks||Mike||16,17|
|40||Tis 2||10:15||Pol_1211||Learning||Mike||20 Probability Theory and Bayesian Network Notes||2. Bayesian Networks|
|40||Fre 5||08:15||Pol_1311||AI in Computer Games||Olle||Lecture Slides|
|41||Mån 8||13:15||Pol_1311||Natural Language Processing||Mike||22,23 Lecture Slides|
|41||Fre 12||15:15||Pol_1311||Conclusion/Recap||Mike||Lecture Slides/Exam Guide|
|43||Mån 22||13:15||Pol_1311||Question Session||Mike||-|
3. Lecture Details
Lecture 1: Introduction
- Course Administration
- Course Overview
- History of AI
Lecture 2: Search 1
- State Space
- Search Trees
- Tree and Graph Search
- Basic Search Strategies: Breadth-First, Uniform-Cost, Depth-First, Iterative Deepening, Bi-directional
- Heuristic Search: Greedy Best-First, A*
Lecture 3: Natural Computation 1
Lecture 4: Natural Computation 2
- Intro to NN - the Perceptron.
- Linear Separability problem.
- Perceptron learning as a minimization problem.
- MLP, NN learning (supervised and unsupervised) and
- generalization, Backpropagation.
- Unsupervised learning - Kohonen's SOMs.
- NN applications.
- GA - intro and terminology.
- A simple GA.
- GA reproductive operators.
- Natural selection and fitness proportional selection - example.
- Other selection techniques.
- Advantages/disadvantages of GA.
- GA applications.
Lecture 5: Search 2 (and Markov Models)
- Hill Climbing
- Simulated Annealing
- Markov Chains/Markov Matrices (in the context of Simulated Annealing)
- Dynamic Programming
Lecture 6: Propositional and First Order Logic
- Semantics (P: Truth Tables and Algebraic. FO: Set-Theoretic and Database)
- Model Searching (P)
- Inference Rules and Proofs
- Substitution and Unification (FO)
- Definite Clauses
- Forward and Backward Chaining
- Possible Worlds and Accessibility Relations (FO)
- Higher Order Logic
- 'Deep' Objections to AI
Lecture 7: Planning
- Actions - Preconditions, Results
- Situational Calculus: Actions as Accessibility Relations
- Planning Domain Definition Language (PDDL)
- Planning Graphs
- Planning as Partial Ordering
- Resource Constraints
Lecture 8: Probability Theory
- Probability Spaces and Basic Probability Theory
- Random Variables and Probability Distributions
- Bayes Law
- Independence and Conditional Independence
- Conditional Probability Distributions
- The Chain Rule
- Expected Utility
Lecture 9: Markov Models and Bayesian Networks
- Markov Chains
- Bayesian Networks
- Markov Models
- Hierarchical Markov Models
- Dynamic Bayesian Networks
Lecture 10: Markov Decision Processes and Influence Diagrams
- Influence Diagrams
- Markov Decision Processes
- State Models (Dynamic Influence Diagrams)
- Partially Observable Markov Decision Processes
- Kalman Filters
- Partially Observable Dynamic Influence Diagrams
- Pattern Matching
Lecture 11: Learning: Soft-sensing and system-control
- Learning Bayesian Networks/Influence Diagrams - Structural and Parametric
- Hidden Variables
- Pattern Learning
- Soft-Sensing Example
- System Control Example
Lecture 12: AI in Computer Games
Lecture 13: Natural Language Processing
Lecture 14: Spare
- If the spare is not required (which is unlikely), we will examine natural language processing in further detail.
- There are 2 obligatory assignments, as listed in the schedule. Detailed instructions for each assignment will follow.
Assignment 1: Planning
- Assignment to be undertaken in groups of (up to) four.
- Groups should be formed by Wednesday, September 19.
- Due Date: NEW DUE DATE OCTOBER 8
- Presentation Dates: TBA
- Details given here
Assignment 2: Bayesian Networks
- Due Date: Friday, October 19
- Details here