Seminar: Basic methods of probabilistic reasoning - Details

Allgemeine Informationen

Veranstaltungsname	Seminar: Basic methods of probabilistic reasoning
Untertitel
Veranstaltungsnummer	8.3273
Semester	WiSe 2019/20
Aktuelle Anzahl der Teilnehmenden	52
Heimat-Einrichtung	LE Cognitive Science
Veranstaltungstyp	Seminar in der Kategorie Offizielle Lehrveranstaltungen
Erster Termin	Donnerstag, 07.11.2019 12:00 - 14:00, Ort: 32/107
Art/Form
Leistungsnachweis	Assessment: presentation + quizzes
SWS	2
Sprache	Englisch
Literatur	As a general reference for the course, we will use Daphne Koller, Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009.
ECTS-Punkte	4

Lehrende

• Dr. rer. nat. Nico Potyka

Räume und Zeiten

32/107

Donnerstag: 12:00 - 14:00, wöchentlich (13x)

Studienbereiche

• Veranstaltungen > Cognitive Science > Master-Programm
• Veranstaltungen > Cognitive Science > Bachelor-Programm
• Courses in English > Human Sciences (e.g. Cognitive Science, Psychology)

Modulzuordnungen

• Bachelor of Science Informatik > CS-BWP-AI - Künstliche Intelligenz, gültig ab WiSe 2019/20
• Bachelor of Science Mathematik > CS-BWP-AI - Künstliche Intelligenz, gültig ab WiSe 2019/20
• Bachelor of Science Cognitive Science > CS-BWP-AI - Künstliche Intelligenz, gültig ab WiSe 2019/20
• Master of Science Mathematik > CS-BWP-AI - Künstliche Intelligenz, gültig ab WiSe 2019/20
• Bachelor of Science Informatik > CS-BWP-NI - Neuroinformatik, gültig ab WiSe 2019/20
• Bachelor of Science Mathematik > CS-BWP-NI - Neuroinformatik, gültig ab WiSe 2019/20
• Bachelor of Science Cognitive Science > CS-BWP-NI - Neuroinformatik, gültig ab WiSe 2019/20
• Master of Science Mathematik > CS-BWP-NI - Neuroinformatik, gültig ab WiSe 2019/20

Kommentar/Beschreibung

Prerequisites: Basic knowledge in computer science and math,
in particular probability theory

Classical reasoning methods are too weak for many practical applications because they cannot deal with uncertainty. Various qualitative (e.g. non-monotonic reasoning) and quantitative (e.g. fuzzy reasoning, possibilistic reasoning) extensions have been proposed to overcome these shortcomings. In this course, we will discuss probabilistic methods that build up on probability theory. For instance, probabilistic logics extend classical logics by replacing the truth values 0 and 1 with the probability interval [0,1]. Reasoning results in classical logic are well founded by the laws of probability theory. However, in order to perform probabilistic reasoning in reasonable time, we often have to make independency assumptions. Probabilistic graphical models represent the independency structure graphically and offer various reasoning and learning algorithms that can exploit this structure. While Probabilistic graphical models can deal with large problems, their logical expressiveness is rather weak. Frameworks from the field of Statistical Relational Artificial Intelligence like Markov Logic and ProbLog can offer a better tradeoff between expressiveness and efficiency.

The first part of the course will consist of lectures, where some of the main formalisms for probabilistic reasoning are introduced. This includes Bayesian and Markov networks, which are the main families of probabilistic graphical models, and propositional probabilistic logic. In particular, we will talk about the basic reasoning (variable elimination, belief propagation) and learning (parameter/structure) methods. In the second part of the course, participants will present advanced topics.