Search:
Business Intelligence 2006

New Date for Final Exam: July 4, 2006, 2-4 pm

According to the regulations of the faculty the originally announced date for the final exam was wrong! The correct Date is:

Tuesday, July 4, 2006, 2-4 pm

Please note:

To be admitted to the final exam, you have to reach at least 50% of the points in each assignment.

 

Introduction

Over the past years, the appearance of applications requiring or benefiting from (classical) artifical intelligence has accelerated. For example, electronic markets for the buying and selling of goods and services over the Web is a fast-growing, multi-billion-dollar segment of the world economy. Knowledge-based techniques for product recommendation, auctions, need identification, vendor selection, negotiation, agent communication, ontologies, business rules, and information integration are of rising interest and have started having practical impact on real Web e-markets.

This class covers the foundational theories (mostly) from the field of (classical) artificial intelligence that have made it possible to evolve to more “intelligent” applications. It will cover areas such as knowledge representation and reasoning (increasingly important through the semantic web effort of the w3c), problem solving, planning, and reasoning under uncertainty. For each of the subjects it will cover the underlying theories and provide a insight into practical applications using those techniques

Please note that though this class does not attempt to answer the grand old question of artificial intelligence: how to build an artificial intelligence. Its goal is to present methods found during this quest that have been surprisingly useful in practical applications.

A full syllabus draft is available as PDF

People

Responsible Lecturer:

Responsible Assistants:

Literature

We will use: Russel, S., & Norvig, P. (2003). Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice-Hall.

Please note: The second edition of the book (published this year is vastly improved). There are some new chapters, that will be available in the library as we need them.

The Readings below are listed as the chapters in the second edition! A conversion table for the chapters can be found here.

Some of the new chapters in the second edition are available on-line

Some additional papers and books. To be announced!

Prerequisites

Required: Finished Grundstudium

Of advantage:

  • KV Datenbanksysteme
  • KV Global verteilte und dynamische Anwendungssysteme

Time Table

Time

Date

Subject

Readings (Chapters)

Assignment

Di

4.4.

Introduction

1

 

 

 

Part 1: Intelligent Search

 

 

 

 

Problem Solving and Planning as Search, Search

3

 

Di

11.4.

Informed Search

4

A1 out

Di

18.4.

Constraint Satisfaction, Adversarial Search

5, 6

 

 

 

Part 2: Knowledge intensive processing

 

 

Di

25.4.

Logic review (Propositional Logic, First Order Logic)

7,8

 

Di

2.5.

Building a knowledge base, Ontologies

10

A1 back

Di

9.5.

Inference, Expert Systems, Knowledge Representation

10

A2 out

 

 

Part 3: Uncertainty, probability, and probabilistic reasoning

 

 

Di

16.5.

Modeling uncertainty - probability revisited

13

 

Di

23.5.

Probabilistic Reasoning: Bayesian Belief Networks

14

 

Di

30.5.

Reasoning over time - (hidden) Markov models

15

A2 back

 

 

Part 4: Learning

 

 

Di

6.6.

Learning Introduction, Tree inducers

18.1-18.3

A3 out

Di

13.6.

Naive Bayes

20.2

 

Di

20.6.

Learning HMM's and Bayesian Belief Networks

20.3

A3 back

 

 

Wrap Up

 

 

Di

27.6.

Summary Session

 

Di

4.7.

Final exam