Probabilistic Artificial Intelligence (Fall ’18)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 upper-level undergraduate and graduate students. VVZ information is available here.
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- First class on Sep 21; first tutorial on Sep 28.
- The lecture video recordings are available at ETH Videoportal.
- No Tutorial classes today (26/10/2018).
- No Regular classes this Friday (30/11/2018). Tutorial classes will be held as normal.
|Friday 10-12||HG E7 (With live video-streaming at E3)|
|Friday 13-14||CHN C14|
|Friday 14-15||CHN C14|
Review SessionApproximate Inference
Markov Decission Models
ExamThe mode of examination is written, 120 minutes length. The language of examination is English. As written aids, you can bring one A4 sheet of paper (you can write on both sides), either handwritten or 11 point minimum font size. Please bring your Legi (ID card) for the exam. Please do not use cellphones / tablets in the exam. Calculators are not allowed. Ph. D. Students can only get credits by passing the exam. You can find previous exams here: , , , , , .
HomeworkThere will be a different homework fortnightly. These will not be corrected and do not count for the final grade. However, we recommend to do them as they will help preparing for the final exam and understanding the concepts.
- S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach (3rd edition).
- Christopher M. Bishop. Pattern Recognition and Machine Learning. [optional]
- Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction.