"Bayesian Algorithms for Self-Driving Cars" is an advanced Computer science course, designed to equip the student with the most important localization algorithms now deployed in modern autonomous vehicles.

This course presents clinicians and digital health enthusiasts with an overview of the data revolution in medicine and how to exploit it for research and in the clinic. The course will not make you a bioinformatician but will introduce the main concepts, tools, algorithms, and databases in this field.

Learn how to think through the ethics surrounding privacy, data sharing, and algorithmic decision-making.

Learn fundamentals of probabilistic analysis and inference. Build computer programs that reason with uncertainty and make predictions. Tackle machine learning problems, from recommending movies to spam filtering to robot navigation.

An introduction to machine learning for healthcare, ranging from theoretical considerations to understanding human consequences of deploying technology in the clinic, through hands-on Python projects using real healthcare data.

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.

Become a data explorer – learn how to leverage data and basic machine learning algorithms to understand the world.

Learn more fundamentals of network security, including cryptographic algorithms used in networking protocols, TLS/SSL, IPSec Layer 2 Security and Wireless Security.

Learn how to design robot vision systems that avoid collisions, safely work with humans and understand their environment.

Take your Python skills further in this online course, guided by the Raspberry Pi Foundation.

We will equip you with everything you need to properly start using Python in your daily work activities. You will learn how to install Python and work with it through different graphical front-ends. You will then learn how to define objects and how to recognize different characteristics and functionalities. Finally, you will learn how to make Python execute a series of instructions in a sequential order through loops, as well as how to write your own functions.

A self-contained course on the fundamentals of modern optimization with equal emphasis on theory, implementation, and application. We consider linear and nonlinear optimization problems, including network flow problems and game-theoretic models in which selfish agents compete for shared resources. We apply these models to a variety of real-world scenarios.