Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Design Optimisation and Orthogonality: Multilinear Regression
Graph Chatbot
Related lectures (30)
Previous
Page 2 of 3
Next
Multicollinearity: Dangers and Remedies
Explores the dangers of multicollinearity in linear models and discusses diagnostic methods and remedies.
Nested Model Selection
Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.
Linear Models: Least Squares
Explores linear models, least squares, Gaussian vectors, and model selection methods.
Introduction to Machine Learning: Linear Models
Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.
Introduction to Machine Learning
Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.
Likelihood Estimation and Least Squares
Introduces simple and multiple normal linear regression, and maximum likelihood estimation with practical examples.
Linear Models: LASSO and AMP
Covers linear problems, LASSO, and AMP in supervised learning, including Generalized Linear Models and N-dimensional models.
Linear Models for Classification
Explores linear models for classification, logistic regression, and gradient descent in machine learning.
Machine Learning Fundamentals: Regularization and Cross-validation
Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.
Non-Convex Optimization: Techniques and Applications
Covers non-convex optimization techniques and their applications in machine learning.