Course material, notes, references, software, manuals, links to external sources such as manuals, etc. are collected in this page and linked below when relevant.
You will need the course book [MN1] and the note [HC1]
from day 1 (or even before for your preparation).
LOOK HERE FOR MORE INFORMATION.
Date  Theme  Reading  Exercises, guidelines, etc.  

1  2sep2008  Introduction to AI and the course; first Prolog workshop Slides1, slides2 (large and strange files) 
[MN] chap 1; [HC1]chapter 1
and parts of 2. Background reading: [AT] 
[HC1], exercise 2.1 and 2.2 Source texts: exercise2_1, circuits. 
2  9sep2008  Prolog workshop continued; Rulebased expert systems;
More workshop: Constraint Handling Rules in Prolog Slides1, Slides2, Slides3.  [MN] chap 2 minus 2.7; [HC1] chap's
2 minus 2.4, chap's 3, 5 and 7. Background reading: [WRZA]; [MN] section 2.7 (nb: example difficult to understand) 
Exercises: [HC1] 5.1, 5.2 (only part), 5.3 + extra presented at lecture,
7.1 and perhaps 7.2
(perhaps more)
Source texts: circuits. vertihori.txt, my_kb0Prolog.txt, expert0.txt, my_kb0.txt, gcd.txt, primes.txt, fibo.txt, kb0_as_CHR.txt. Alternative versions (tested in SICStus 4 and should work in SWI): expert04.txt, gcd4.txt, primes4.txt, fibo4.txt, kb0_as_CHR4.txt. 
3  16sep2008  Deduction, Induction, and Abduction  with special emphasis on abduction.
Application of abduction to diagnosis problem. Slides. 
[HC1]
chapters 7, 8 minus 8.9. Background reading: (alternative description of the above) [HC2] pages 117 (middle); 
[HC1] the exercise of section 9.2, question 1.
Exercise about CHR. Source texts: db.txt, diagnosisPeriodic.txt, diagnosisConsistent.txt, deductivePower.txt, toolsCHRexercise.txt. 
4  23sep2008  Statistics and Bayesian reasoning, Bayesian networks Slides 
[MN] chapter 3, until p. 62 middle (check comments in the note below:) Examples and exercises for conditional probabilities and Bayesian reasoning Charniak: Bayesian Networks without Tears. AI Magazine 12(4): 5063 (1991); skip from p. 53 "In the rest of this section, I define..." until the section headed "Consistent probabilities" p. 55. Skip also from p. 56 "Evaluation networks" and the rest of the paper. Notice the error in fig. 2: a negation sign missing; it should read P(do  ¬ fo ¬ bd) = .3 
Exercises of Examples and exercises for conditional probabilities and Bayesian reasoning 
5  30sep2008  More on probabilistic methods:
The PRISM
system, implementing Bayesian networks. Slides as pdf 
Course notes: NEW VERSION 29sep2008:
LogicalStatistical models and parameter learning
in the PRISM system, minus section 3 (optional for those with an
interest in biological sequence data).
Complementary reading (optional): Hidden Markov Models and their implementation in the PRISM system 
Exercise 5.2 of LogicalStatistical models and parameter learning
in the PRISM system. Source texts: famOut.psm, famOutData.dat, hmm1.psm, hmm2.psm, hmm3.psm, words.dat. 
6  7oct2008  Natural Language Analysis with Definite Clause Grammars;
discourse analysis with CHR.
Starting the written assignments 
[BBS]. (pdf document) chapters 7 and 8, minus 7.2.3 and 8.1.3; Natural language analysis with DCG and CHR: Examples and exercises  All exercises of the course note.
NB: More exercises to be added Source texts: dcg1, dcg2, trip, discourse1, discourse2. 
7  14oct2008  More on Natural Language Analysis: Assumptions in Hyprolog 
Natural language analysis
with assumptions in Hyprolog:
Introduction, examples and exercises.
User's guide to the HYPROLOG system: A logic programming language with assumptions and abduction
We will use the Hyprolog system which you can download
here.

Exercises of course note
Natural language analysis
with assumptions in Hyprolog:
Introduction, examples and exercises. Notice that the note also contains a proposal for a written assignment by extending one of the natural language examples. Source text: simpleAssump, solutionEx2 (solution to an exercise). 
8  21oct2008  Written assignment  No normal course session but your teacher will be available (at his office) all day for helping you with the assigments.  
9  28oct2008  Artificial neural networks Powerpoint slides by Angshuman Saha. 
[MN] chap. 6, until and including 6.5; the rest of chap. 6 should be viewed as background material.
NB: You are not expected to be able to reproduce the details of the book's formulas for adjusting weights,
but you should understand the overall feedforward, backpropagation mechanism. We will also look at one or both of two tools for building and training neural nets which you can find at http://www.geocities.com/adotsaha/NNinExcel.html, produced by Angshuman Saha; you should read this page and the explanations provided by the two tools. NB: These tools run as Excel sheets, so they should be easy to run open and test. 
Exercises: Note with exercises about Neural Networks, question 7.17.4; if time 7.5. Questions 7.57.6 recommended as homework. 
10  4nov2008  Fuzzy expert systems Student presentation of the status for their written assignments 
[MN] chap 4; see important notes here. Backgrund reading: [BNW] gives an alternative presentation of fuzzy logic and control. 
Exercises about Fuzzy Control: Driving car using fuzzy logic; exercise 13, perhaps 4 and 5 if time permits. 
12nov2008  LAST DAY FOR GIVING IN THE WRITTEN ASSIGNMENT  Send by email to your teacher  
11  11nov2008  Evolutionary computing
(We use parts the textbook's slides, lectures 9+10 
[MN] chap. 7 Skip section 7.4, and be aware that there are several
problematic and unclear points in this chapter; see comments.
Background reading: The following article lists interesting applications of EC/GP. J.R. Koza, M.A. Keane, M. J. Streeter: Evolving Intentions. Scientific American, Feb 2003, Vol. 288, Issue 2 (pp. unknown). Available online when you search via http://www.rub.ruc.dk (works only from RUC or VPN to RUC). You may also search for references concerning antenna design, where EC/GP has had success. 
Exercises on genetic programming. 
QA  ??jan2009 10.0014.00 (?) 
Questionandanswer session
Preparation for the exam 
Please send questions or topics you want explained in advance to your teacher. If no questions or indications of interest have been received by a deadline to be specified, this session is cancelled.  
X  5jan2009  EXAM 
About the exam Sometime during the course (probably near the end), each student will prepare a written assigment, which is handed in and given to the examiner/external examiner. There will be a slot of total 30 min's for the exam of each student, including examiner's evaluation. Examination may take about 1520 min's, and you are expected to start with a presentation of your written assignment of perhaps 5 to 7 min's, which will be followed by a discussion. The examiners will most likely also ask a question to part of the course literature which is not directly related to your assignment but can be anywhere in the course curriculum. An advice: The presentation of the assignment needs not be fancy, it's the content that matters. A possible structure could be: Which problem did I approach, how did I approach it, what did work and what did not, and what did I learn .... Another advice: The report that you gave in for the assignment is not in itself assessed in the exam, so if you you are aware of any weak points, you need not "defend" or "repair" but you can (if you like) take these as points for discussion. If there is any doubt, contact your teacher. 