Computational Probability and Inference
Probability and inference are used everywhere. For example, they help us figure out which of your emails are spam, what results to show you when you search on Google, how a self–driving car should navigate its environment, or even how a computer can beat the best Jeopardy and Go players! What do all of these examples have in common? They are all situations in which a computer program can carry out inferences in the face of uncertainty at a speed and accuracy that far exceed what we could do in our heads or on a piece of paper. In this data analysis and computer programming course, you will learn the principles of probability and inference. We will put these mathematical concepts to work in code that solves problems people care about. You will learn about different data structures for storing probability distributions, such as probabilistic graphical models, and build efficient algorithms for reasoning with these data structures. By the end of this course, you will know how to model real–world problems with probability, and how to use the resulting models for inference. You don’t need to have prior experience in either probability or inference, but you should be comfortable with basic Python …
Courses : 1
Specification: Computational Probability and Inference
2 reviews for Computational Probability and Inference
Add a review Cancel reply
This site uses Akismet to reduce spam. Learn how your comment data is processed.
This is an excellent course with very clear lecture videos and comprehensive class notes. The instructors have a high degree of mastery of the subject and are able to communicate clearly and concisely. Numerous, interesting problem sets range from straightforward to challenging. This is a high quality MOOC. Looking forward to the second part of the course 6.008.2x when it becomes available.
The course is super cool. The subject is hard but the instructors have a deep knowledge of the subject. Teaching style is great and it combines a great accuracy with clear examples. I would definitely recommend it to anyone who is interested in learning Probability.