Teaching, Tutorials, Notes
An overview of courses currently offered by MLR is given on our MLR pages. Here you find full slide collections and scripts for my courses, and at the bottom of this page:Full slide collections and scripts:
 Introduction to Machine Learning
 Introduction to Robotics
 Introduction to Artificial Intelligence
 Maths for Intelligent Systems (Brief reference for Linear Algebra, Optimization & Probabilities)
 Introduction to Optimization
Courses & Tutorials
Introduction to Machine Learning (SS 19)The Machine Learning course first covers basic regression and classification methods (e.g. Bayesian Kernel Ridge Logistic Regression...) and then focusses on Bayesian formulations of learning (Bayes nets, probabilistic inference) In Stuttgart I plan to iterate the course every summer. Previous versions are: 
 WS 18/19  Maths for Intelligent Systems
 (Previous versions: WS 16/17, WS 15/16)
 WS 18/19  Artificial Intelligence Bachelor Course
 (Previous versions: WS 16/17, WS 15/16, WS 14/15)
 SS 15  Optimization
 (Previous versions: SS 14, SS 13)
 WS 14/15  Artificial Intelligence Bachelor Course
Introduction to Robotics (WS 14/15)The Robotics course covers the basics of motion generation (kinematics, dynamics, planning, control) as well as state estimation (in mobile robotics). In Stuttgart I plan to iterate the course every winter. Previous versions are: 
 WS 14/15  Hauptseminar: Machine Learning
 SS 14  Hauptseminar: Robotics
 WS 13/14  Hauptseminar: Machine Learning
 WS 13/14  Foundations of Autonomous Systems
 SS 13  Hauptseminar: Topics in Robotics
Tutorials
 Bandits, Global Optimization, Active Learning, and Bayesian RL  understanding the common ground [old version]
 A brief (90mins) tutorial held first at the Machine Learning Summer School, Tübingen, Sep 2013; and later at the Autonomous Learning Summer School, Leipzig, Sep 2014. The aim is to introduce to various problems from the perspective of belief planning and discuss what optimal policies would be. Pointers to more indepth literature are provided. See a video here.
 ICML 2011 Tutorial on Machine Learning & Robotics
 Machine Learning tutorial at the Interdisciplinary College 2011
 The 3 basic lectures target an interdiciplinary audience
(students from Computer Sci, Cog Science, Neuroscience, Psychology),
covering basics in ML, Bayesian Modelling, and RL:

1. Introduction
 2. Linear Models (nonlinear features, regularization, crossvalidation, `linear/polynomial/kernel Ridge/Lasso regression/logistic classification')
 3. Bayesian Modelling (Bayes, examples, regularization & prior, error & likelihood, MAP view on Ridge/Lasso regression, EM, Bayes Nets)
 4. Reinforcement Learning (Markov Decision Process, values, temporal difference, modelfree vs. modelbased, planning by probabilistic inference)

1. Introduction
 BCCN lecture Computational models of goaldirected behavior
 slides exercise.
 RLSS 09  Inference & Planning

Lectures given at the Robot Learning Summer
School (Lisbon, July 2024 2009).
Slides: part 1, part 2 Part 1: Introduction to probabilistic inference \& learning
  probabilities, joint distributions, graphical models  inference, message passing  learning, Expectation Maximization
 Part 2: Planning by Inference
  general idea of inference by planning  Markov Decision Processes revisited  Stochastic Optimal Control revisited
 Summary & further reading
  brief summary  further reading  food for thought
 ICML 08 tutorial  Stochastic Optimal Control
 Tutorial, held together with Bert Kappen on Saturday July 5 2008 in Helsinki, Finland as part of the 25th International Conference on Machine Learning (ICML 2008). See the tutorial web page.
Interesting Readings
 Pedro Domingos: A few useful things to know about machine learning. Communications of the ACM, 2012.
 Anil Ananthaswamy: I, algorithm: A new dawn for artificial intelligence. A popular science article in NewScientist, 2011. (another link)
 Pat Langley: The changing science of machine learning. Editorial in Machine Learning 82, 275279, 2011.
 Thomas G. Dietterich et al.: Structured machine learning: the next ten years. Machine Learning, 73, 323, 2008.
 Yoshua Bengio & Yann LeCun: Scaling learning algorithms towards AI. LargeScale Kernel Machines, 34, 2007.
 Rodney Douglas, Terry Sejnowski & others: Future Challenges for the Science and Engineering of Learning. Report of an NSF workshop, 2007.
 Tom Mitchell: The Discipline of Machine Learning. Report CMUML06108, Carnegie Mellon University, 2006.
 Leo Breiman: Statistical modeling: The two cultures. Statistical Science, 2001.
Reference Material
 Linear algebra references

 My lecture notes on Maths for Intelligent Systems
 Stanford teaching material linalg
 (Standard Reference) Gilbert Strang: Linear Algebra and its Applications and its MIT Open Courseware site
 Duda, Hart, Stork: Pattern Classification (Chapter A gives a very brief review of linear algebra and probabilities) here and here
 (Coordinatefree linear algebra) Sadri Hassani: Mathematical Physics: A Modern Introduction to its Foundations here
 Chapters 7 and 8 in Erwin Kreyszig: Advanced Engineering Mathematics here
 Gilbert Strang: Linear Algebra and its Applications
 Zico Kolter: Linear Algebra Review and Reference
 Gilbert Strang's Open Courseware site
 Gilbert Strang: Introduction to Linear Algebra
 Coordinatefree linear algebra: Mathematical Physics: A Modern Introduction to its Foundations, Sadri Hassani
 Coordinatebased matrix linear algebra: Erwin Kreyszig: Advanced Engineering Mathematics
 Probabilities & Machine Learning
 Optimization