Linear Regression: Parametric Models and Optimization

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (52)

Linear Regression: Multicollinearity, Outliers, Model Specification Log in to Mediaspace to watch this video

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

Data-Driven Modeling: Regression Log in to Mediaspace to watch this video

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

Untitled Log in to Mediaspace to watch this video

Machine Learning in Molecular Dynamics Log in to Mediaspace to watch this video

Explores the application of machine learning in molecular dynamics and materials, emphasizing the creation of meaningful features and the importance of generalizability.

Statistics: Exploratory Data Analysis Log in to Mediaspace to watch this video

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Linear and Logistic Regression Log in to Mediaspace to watch this video

Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.

Linear Regression: Model Adjustment and Parameter Estimation Log in to Mediaspace to watch this video

Explains the decomposition of total sum of squares, model adjustment, and parameter estimation in linear regression.

Modeling Response Surfaces in Matlab Log in to Mediaspace to watch this video

Covers modeling response surfaces in Matlab, including building the model, defining the domain, computing values, and analyzing residuals.

Document Analysis: Topic Modeling Log in to Mediaspace to watch this video

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Method of Least Squares: Normal Equations Log in to Mediaspace to watch this video

Explains the method of least squares and normal equations for finding optimal solutions.

Linear Models: Part 2 Log in to Mediaspace to watch this video

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

Orthogonal Projections: Gram-Schmidt Method Log in to Mediaspace to watch this video

Explores orthogonal projections and the Gram-Schmidt method for constructing bases.

Page 3 of 3