Linear Regression: Theory and Applications

Related lectures (29)

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Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Covers the normal distribution, including its properties and calculations.

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

Covers supervised learning through likelihood maximization to find optimal parameters.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

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

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

Covers the basics of probability theory, random variables, joint probability, and inference.