Teaching, Tutorials, Notes
Here you find full slide collections and scripts for my courses, and at the bottom of this page: Tutorials, Readings, Reference Material, Older lecture notes.
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
- WS 20/21 - Optimization Algorithms
- (Previous versions: SS 14, SS 13)
- SS 20 - AI & Robotics: Research
- See general information here.
- SS 20 - AI & Robotics: Lab Course
- See general information here.
- WS 19/20 - Maths for Intelligent Systems
- (Previous versions: WS 18/19, WS 16/17, WS 15/16)
- WS 19/20 - Artificial Intelligence Bachelor Course
- (Previous versions: WS 18/19, WS 16/17, WS 15/16, WS 14/15)
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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: |
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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
- Brief Intro to Gaussian Processes
- SimTech ML Seminar, Feb 5 2020.
- 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 in-depth 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 (non-linear features, regularization, cross-validation, `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, model-free vs. model-based, planning by probabilistic inference)
-
1. Introduction
- BCCN lecture Computational models of goal-directed behavior
- slides exercise.
- RLSS 09 - Inference & Planning
-
Lectures given at the Robot Learning Summer
School (Lisbon, July 20-24 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
- Part 1: Introduction to probabilistic inference \& learning
- 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, 275-279, 2011.
- Thomas G. Dietterich et al.: Structured machine learning: the next ten years. Machine Learning, 73, 3-23, 2008.
- Yoshua Bengio & Yann LeCun: Scaling learning algorithms towards AI. Large-Scale 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 CMU-ML-06-108, 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
- (Coordinate-free 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
- Coordinate-free linear algebra: Mathematical Physics: A Modern Introduction to its Foundations, Sadri Hassani
- Coordinate-based matrix linear algebra: Erwin Kreyszig: Advanced Engineering Mathematics
- Probabilities & Machine Learning
- Optimization