Difference between revisions of "Cognitive Robotics"
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* [http://www.dei.polimi.it/people/matteucci Matteo Matteucci]: the teaching assistant | * [http://www.dei.polimi.it/people/matteucci Matteo Matteucci]: the teaching assistant | ||
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===Course Program=== | ===Course Program=== | ||
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* '''Stochastic models''': basics, optimization of models, fitness function, model definition, genetic algorithms, reinforcement learning, bayesian networks; | * '''Stochastic models''': basics, optimization of models, fitness function, model definition, genetic algorithms, reinforcement learning, bayesian networks; | ||
* '''Applications''': motivations, choices, models, case studies. | * '''Applications''': motivations, choices, models, case studies. | ||
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| + | ===Teaching Material=== | ||
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| + | Here you find a copy of the slide of the teaching assistant, the slides for the whole course are available [http://home.deib.polimi.it/gini/robot/lezionir2.htm here] | ||
| + | * Slides on [[Media:CognitiveRobotics_01_RoboticsMiddleware.pdf | Robotics Middleware]] | ||
| + | * Slides on [[Media:CognitiveRobotics_02_ROS_Introduction.pdf | ROS Introduction]] | ||
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Revision as of 01:37, 25 March 2014
I am the Teaching Assistant of the Cognitive Robotics course; the official site of the course is not maintained by me and it can be found here. On this page I am publishing the material of my lectures for this class but you can find those also on the official course page.
Course Aim & Organization
Teachers
The course is composed by a blending of lectures and exercises by the course teacher and the teaching assistant:
- Giuseppina Gini: the teacher
- Matteo Matteucci: the teaching assistant
Course Program
- What is Soft Computing: fuzzy systems, neural networks, stochastic algorithms and models;
- Fuzzy models: fuzzy sets, fuzzy logic, fuzzy rules, motivations for fuzzy modeling;
- Neural networks: basics, supervised and unsuprvised learning, main modelsi, selection and evaluation;
- Stochastic models: basics, optimization of models, fitness function, model definition, genetic algorithms, reinforcement learning, bayesian networks;
- Applications: motivations, choices, models, case studies.
Teaching Material
Here you find a copy of the slide of the teaching assistant, the slides for the whole course are available here
- Slides on Robotics Middleware
- Slides on ROS Introduction
