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.
Lernziel
How can we build systems that perform well in uncertain environments? 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 robotics. The course is designed for graduate students.
Solid basic knowledge in statistics, algorithms and programming. The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.
Kompetenzen
Fachspezifische Kompetenzen
Konzepte und Theorien
geprüft
Verfahren und Technologien
geprüft
Methodenspezifische Kompetenzen
Analytische Kompetenzen
geprüft
Entscheidungsfindung
geprüft
Medien und digitale Technologien
geprüft
Problemlösung
geprüft
Projektmanagement
geprüft
Soziale Kompetenzen
Kommunikation
gefördert
Kooperation und Teamarbeit
gefördert
Persönliche Kompetenzen
Kreatives Denken
geprüft
Kritisches Denken
geprüft
Integrität und Arbeitsethik
gefördert
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Die Leistungskontrolle wird nur in der Session nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung möglich.
Prüfungsmodus
schriftlich 120 Minuten
Zusatzinformation zum Prüfungsmodus
100% of the final grade is determined by the session examination. As a compulsory continuous performance assessment task, the project component of the course must be passed on it's own to take the exam.
The practical projects are an integral part (60 hours of work, 2 credits) of the course. The project component is assessed on a pass/fail basis. 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 component are required to de-register from the exam and will otherwise be treated as a no show.
Due to the number of registered students, the exam may be paper-based and will most likely take place on a Saturday. The mode of the exam (computer-based or paper-based) will be finalized in end of October, and the exam date will be announced in December.
Hilfsmittel schriftlich
Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. Simple non-programmable calculator.
Digitale Prüfung
Die Prüfung findet auf Geräten statt, die von der ETH Zürich zur Verfügung gestellt werden.
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.