Math for Intelligent Systems
Theoretical and Methodological Foundations of Autonomous Systems
Welcome to the University of Stuttgart "Math for Intelligent System" lecture page. These lectures will be part of the Master's winter term 2017/18, and will be based on:
 Prof. Marc Toussaint's script
 Nathan Ratliff's lecture material
The course is listed in c@mpus under the name "Theoretical and Methodological Foundations of Autonomous Systems/Mathematics for Intelligent Systems", however we prefer the short "Math for Intelligent Systems" and will refer to it accordingly. So don't get confused.
Students are required to have solid knowledge in linear algebra, probability theory and optimization. Fluency in at least one higher level programming language (MATLAB/GNU Octave, Python, etc.) is highly recommended.
Organizational matters
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Lecturers: Jim Mainprice, Ph.D., Heiko Zimmermann, M.Sc.
Lessons: Thurs 17:3019:00 at room 0.457
Tutorial: Tue 17:3019:00 at room 0.457
Exam: 22.02.2018, 11:0013:00 in room V38.04
Homework assignments are weekly. The solutions will be discussed during the Tutorial sessions. In order to take the final exam at the end of the semester you need to have done at least 50% of the homework exercises.
Further Material
What is this lecture about
The "Maths for Intelligent Systems" course will recap essentials of linear algebra, optimization, probabilities, and statistics in order to equip students with the basics of speaking maths to formulate problems in intelligent systems research.
"The point of this lecture is to teach you to speak maths, to use maths to describe systems or problems. I feel that most maths courses rather teach to consume maths, or solve mathematical problems, or prove things. Clearly, this is also important. But for the purpose of intelligent systems research, it is essential to be skilled in expressing problems mathematically, before even thinking about resulting algorithms.
"  Prof. Dr. rer. nat. Marc Toussaint (Head of MLR, University of Stuttgart)
Lectures and materials
Topic
Lecture
Tutorial
Notes
Exercise
 Linear Algebra I: Vector Spaces, Bases, Matrix
24.10.2017
26.10.2017
Read: 2.1, 2.2, 2.3
 Linear Algebra II: Projections and The Fundamental Structure of Linear Transforms
02.11.2017
07.11.2017
Read: 2.4, 2.5, 2.6.1
 Linear Algebra III: SVD, Fundamental Structure of Linear Transforms
09.11.2017
14.11.2017
Read: 2.4, 2.6, Appendix B
CLASS CANCELED
 Gradients and Hessians I
23.11.2017
28.11.2017
Read: 3.18
Discussion of homework 4 (ex. 2) and 5
 Christmas & New Year



no homework
 Recap
15.02.2018


