Linear Regression & Correlation

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Related lectures (32)

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Modeling Bivariate Data: Linear Regression

Explores bivariate data, linear regression modeling, and correlation in scientific investigations and research processes.

Understanding Data Attributes

Covers the analysis of various data attributes and linear regression models.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Supervised Learning: Linear Regression

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

Linear Regression: Multicollinearity, Outliers, Model Specification

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Linear Models: Part 2

Covers linear models, binary and multi-class classification, and logistic regression with practical examples.

Introduction to Data Science

Introduces data science topics, evaluation methods, and course organization for the semester.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Data-Driven Modeling: Regression

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.