Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Deterministic Part: Utility Function Coding
Graph Chatbot
Related lectures (47)
Linear Regression and Logistic Regression
Log in to Mediaspace to watch this video
Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Binary Response: Link Functions
Log in to Mediaspace to watch this video
Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.
Maximum Likelihood Estimation: Theory
Log in to Mediaspace to watch this video
Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.
Logistic Regression: Probabilistic Interpretation
Log in to Mediaspace to watch this video
Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.
Linear Models: Continued
Log in to Mediaspace to watch this video
Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Linear Models for Classification: Logistic Regression and SVM
Log in to Mediaspace to watch this video
Covers linear models for classification, focusing on logistic regression and support vector machines.
Convex Optimization: Examples of Convex Functions
Log in to Mediaspace to watch this video
Explores convex optimization, convex functions, and their properties, including strict convexity and strong convexity, as well as different types of convex functions like linear affine functions and norms.
Linear Models for Classification: Multi-Class Extensions
Log in to Mediaspace to watch this video
Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.
Generalized Linear Models: A Brief Review
Log in to Mediaspace to watch this video
Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.
Linear Regression Basics
Log in to Mediaspace to watch this video
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion
Log in to Mediaspace to watch this video
Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.
Linear Models: Classification Basics
Log in to Mediaspace to watch this video
Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.
Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation
Log in to Mediaspace to watch this video
Explores Maximum Likelihood Estimation, linear models, logistic regression, and Support Vector Machines.
Support Vector Machines: Basics and Applications
Log in to Mediaspace to watch this video
Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.
Linear Regression: Basics and Gradient Descent
Log in to Mediaspace to watch this video
Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.
Untitled
Log in to Mediaspace to watch this video
Logistic Regression: Probability Modeling
Log in to Mediaspace to watch this video
Covers logistic regression for binary classification using probability modeling and optimization methods.
Regression Trees and Ensemble Methods in Machine Learning
Log in to Mediaspace to watch this video
Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.
General Linear Model: Model Selection
Log in to Mediaspace to watch this video
Explores the General Linear Model, significance testing, model selection, and parameter inference.
Signals & Systems II: Complex Random Vectors and Stationarity
Log in to Mediaspace to watch this video
Explores complex random vectors, stationarity, ergodicity, and the analysis of signals.
Previous
Page 2 of 3
Next