This course provides an introduction to probability theory. You will encounter discrete and continuous random variables and learn in which situations they appear, what their properties are and how they interact.
Welcome to "Probability Theory: Foundation for Data Science," an intermediate-level course designed to provide a comprehensive review of probability theory essential for success in engineering and data science fields. This course, offered by DelftX, is part of the Professional Certificate in Mastering Probability and Statistics. It's a self-contained, modular program that allows you to focus on specific areas of interest while refreshing your bachelor-level mathematics knowledge.
This course offers a unique opportunity to strengthen your foundation in probability theory, a critical skill for success in various engineering and data science fields. With its flexible, self-paced format and access to numerous exercises through the Grasple platform, you'll be able to practice and reinforce your learning with intelligent, personalized feedback. Don't miss this chance to elevate your mathematical prowess and unlock new possibilities in your academic or professional journey!
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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.
'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.
The first in a series of four courses which help you to master statistics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate 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.
