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Business Intelligence

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

Subject

 

Readings (Chapters)

Assignment

Mo

20.10.

Introduction

 

 

 

 

Intelligent search

 

 

 

 

Heuristic Methods, Constraint Satisfaction

3 & 4

 

Mo

27.10.

Scheduling, Routing and Games

5 & 6

A1 out

 

 

Knowledge intensive processing

 

 

Mo

3.11.

Logic review (Propositional Logic, First Order Logic)

skim 7, 8

 

Mo

10.11.

Building a knowledge base, Ontologies

9 & 10

A2 back

Mo

17.11.

Inference, Expert Systems

 

A2 out

Mo

24.11.

Automated Contracting, Semantic Web, Guest: Klaus Bena - EDS

 

 

 

 

Learning

 

 

Mo

1.12.

Tree inducers

18 (18.1, 18.2, 18.3)

 

Mo

8.12.

Bayesian Methods

13 (20.1, 20.2)

.

Mo

15.12.

Spam filtering, fraud detection, house price estimation


 A2 back

 

 

X-Mass break

 

 

 

 

Probabilistic learning & reasoning

 

 

Mo

5.1.

Bayesian Belief Networks – Clippy and Co.,
Guest: Dr. Donato Scognamiglio, IAZI AG

14 (in particular beginning < 14.5)

A3 out

Mo

12.1.

Hidden Markov Models – Speech Recognition

We skipped this subject!

 

 

 

Natural Language Processing

 

 

Mo

19.1.

Text Mining, Information Extraction
Guest:
Dr. Manfred Klenner, Institut für Computerlinguistik, Universität Zürich

 

A3 back

Mo

26.1.

Machine Translation
Guests:
Dr. Susanne Jekat, Zürcher Fachhochschule Winterthur, Zürich
Monika Röthlisberger, CLS Corporate Language Services, Zürich/Basel/Bern

 

 

Mo

2.2.

Summary session

 

 

Mo

9.2.

Final exam

 

 

Handouts

Slides

Assignments