Lectures related to Electron Transfer Theory

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Random Variables and Expected Value

Introduces random variables, probability distributions, and expected values through practical examples.

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Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Fundamental Limits of Gradient-Based Learning

Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.

Continuous Random Variables

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Discrete Random Variables

Covers properties and transformations of discrete random variables, focusing on PMF and expectation.

Probability and Statistics: Discrete Random Variables

Explores discrete random variables, mass functions, and distribution functions in probability and statistics.

Statistics for Data Science: Basics and Modelling

Introduces statistical modelling basics, probability theory, and key concepts for data science applications.

Statistical Inference: Random Variables

Covers random variables, probability functions, expectations, variances, and joint distributions.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Probability and Statistics

Explores joint random variables, conditional density, and independence in probability and statistics.

Linear Regression: Theory and Applications

Covers the theory and practical applications of linear regression.

Probability Theory: Discrete Random Variables

Covers discrete random variables, probability mass function, properties, and binomial distribution with illustrative examples.

Random Variables: Expected Value

Covers advanced probability concepts, including random variables and expected value calculation.

Probability Theory: Midterm Solutions

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Probability Theory: Random Variables and Independence

Explores discrete and continuous random variables, independence, and probability functions.

Variance of Random Variables

Covers the concept of variance for random variables and introduces calculation rules.

Continuous Random Variables: Basics

Explores continuous random variables, cdf, pdf, convolution, and sum of dice.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.