This course discusses properties and applications of random variables. For instance, how many customers are likely to arrive in the next hour? What’s the probability that a lightbulb will last more than a year?
Dive deep into the fascinating world of random variables with this intermediate-level course offered by GTx. This comprehensive program explores the properties and applications of both discrete and continuous random variables, equipping you with the essential tools for a wide range of modeling and analysis problems. As you progress through the course, you'll develop a strong foundation in probability theory and statistics, preparing you for more advanced studies in the field.
To succeed in this course, students should have:
Lessons 1-12 covering topics from introduction to honors bonus results
Lessons 1-17 covering topics from introduction to honors bivariate functions of random variables
Throughout the course, students will engage with the R statistical package, enhancing their practical skills in data analysis and probability calculations. This combination of theoretical knowledge and practical application makes the course an invaluable asset for anyone looking to advance their career or academic pursuits in fields related to data analysis, statistics, and probability.
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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.
This course discusses properties and applications of random variables. For instance, how many customers are likely to arrive in the next hour? What’s the probability that a lightbulb will last more than a year?
When you’re done with this course, you’ll have enough firepower to undertake a wide variety of modeling and analysis problems; and you’ll be well-prepared for the upcoming Statistics courses.
'Statistical and Probabilistic Foundations of AI' provides an accessible overview of the mathematics and statistics behind fundamental concepts of data science, machine learning, and artificial intelligence. It covers descriptive and exploratory data analysis and a brief introduction to inferential statistics. It provides the principles of probability necessary to understand the methods used in inferential statistics and machine learning at an introductory level.
This course provides an introduction to basic statistical concepts. We begin by walking through a library of probability distributions – including the normal distribution, which in turn leads to the Central Limit Theorem. We then discuss elementary descriptive statistics and estimation methods.
