Teaching

The course provides an introduction to probability, as a language and set of tools for understanding statistics, science, risk, and randomness. The following topics are covered:

• Probability and counting;

• Conditional probability and Bayes' rule;

• Discrete random variables;

• Continuous random variables;

• Joint distributions;

• Conditional expectation;

• Transformations;

• Inequalities and limit theorems.

The course provides an introduction to the mathematical theory behind statistical methods and theoretical guarantees for the statistical methods that you may use for certain applications of engineering and science. The following topics are covered:

• Models, likelihood, and estimation;

• Point estimation;

• Interval estimation;

• Hypothesis testing;

• Bayesian statistics.

The course provides an introduction to the main tools of linear systems design in the different fields of engineering.

The course provides an introduction to the main computational modeling tools for understanding the brain (principles, building blocks, functions) and the main technologies for measuring and controlling brain activity.

The course provides an introduction to advanced informatics tools to collect and interpret information from sensors, and to plan and perform actions based on this information. The following topics are covered:

• Robot direct and inverse kinematics;

• Control and planning methods;

• Sensor interpretation;

• SLAM: Simultaneous Localization and Mapping;

• Applications: Mobile robots + arms and grippers.