263-5210-00L Probabilistic Artificial Intelligence
Semester | Herbstsemester 2020 |
Dozierende | A. Krause |
Periodizität | jährlich wiederkehrende Veranstaltung |
Lehrsprache | Englisch |
Lehrveranstaltungen
Nummer | Titel | Umfang | Dozierende | |||||||
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263-5210-00 V | Probabilistic Artificial Intelligence The lectures will mostly be given in a lecture hall with limited attendance (at most 50% of lecture hall capacity). It will be possible to join remotely via zoom with acccess to slides, whiteboard, and speaker camera. Students can interact, e.g. ask questions, physically as well as digitally. The lectures will be recorded via zoom’s recording functionality. | 3 Std. |
| A. Krause | ||||||
263-5210-00 U | Probabilistic Artificial Intelligence | 2 Std. |
| A. Krause | ||||||
263-5210-00 A | Probabilistic Artificial Intelligence | 2 Std. | A. Krause |
Katalogdaten
Kurzbeschreibung | This course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics and the Internet. |
Lernziel | How can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for graduate students. |
Inhalt | Topics covered: - Probability - Probabilistic inference (variational inference, MCMC) - Bayesian learning (Gaussian processes, Bayesian deep learning) - Probabilistic planning (MDPs, POMPDPs) - Multi-armed bandits and Bayesian optimization - Reinforcement learning |
Voraussetzungen / Besonderes | Solid basic knowledge in statistics, algorithms and programming. The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite. |
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
ECTS Kreditpunkte | 8 KP |
Prüfende | A. Krause |
Form | Sessionsprüfung |
Prüfungssprache | Englisch |
Repetition | Die Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich. |
Prüfungsmodus | schriftlich 120 Minuten |
Zusatzinformation zum Prüfungsmodus | 70% session examination, 30% project; the final grade will be calculated as weighted average of both these elements. As a compulsory continuous performance assessment task, the project must be passed on its own and has a bonus/penalty function. Die Prüfung kann am Computer stattfinden / The exam might take place at a computer. The practical projects are an integral part (60 hours of work, 2 credits) of the course. Participation is mandatory. Failing the project results in a failing grade for the overall examination of Probabilistic Artificial Intelligence (263-5210-00L). Students who do not pass the project are required to de-register from the exam and will otherwise be treated as a no show. |
Hilfsmittel schriftlich | Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. Simple non-programmable calculator. |
Online-Prüfung | Die Prüfung kann am Computer stattfinden. |
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan. |
Lernmaterialien
Hauptlink | Information |
Es werden nur die öffentlichen Lernmaterialien aufgeführt. |
Gruppen
Keine Informationen zu Gruppen vorhanden. |
Einschränkungen
Plätze | Maximal 700 |
Warteliste | Bis 28.09.2020 |