Mathematical understanding of uncertainty
About this course
This lecture series discusses how the concept of probability can be used to handle, control, and exploit uncertainty in the real-world. It is an undergraduate-level lecture series on probability, but is entirely different from the usual courses on probability theory. The lectures cover the basics of probability theory including the relevant mathematics, but instead of focusing on mathematics, the lectures explain how probability theory can help understand real-world uncertainty using various examples. The examples are used to describe how uncertainty can be exploited to implement modern randomized algorithms such as Markov chain Monte Carlo and deep learning.
75/100
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- What the provider tells you
- 24/45
- Who stands behind it
- 35/35
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- 16/20
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What you'll learn
- understanding key concepts of probability theory
- applying probability to real-life uncertainty
- using probability in modern algorithms
- exploring examples of randomized algorithms
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