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
Linear Models: Classification
Graph Chatbot
Related lectures (55)
Supervised Learning Essentials
Log in to Mediaspace to watch this video
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Linear Models: Part 1
Log in to Mediaspace to watch this video
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Linear Models: Basics
Log in to Mediaspace to watch this video
Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.
Classification Algorithms: Generative and Discriminative Approaches
Log in to Mediaspace to watch this video
Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.
Linear Models & k-NN
Log in to Mediaspace to watch this video
Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.
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.
Linear Models: Continued
Log in to Mediaspace to watch this video
Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.
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.
Machine Learning Fundamentals: Regularization and Cross-validation
Log in to Mediaspace to watch this video
Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.
Supervised Learning: Classification Algorithms
Log in to Mediaspace to watch this video
Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
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.
Logistic Regression: Classification
Log in to Mediaspace to watch this video
Covers supervised learning, classification using logistic regression, and challenges in optimization.
Supervised Learning Fundamentals
Log in to Mediaspace to watch this video
Introduces the fundamentals of supervised learning, including loss functions and probability distributions.
Deep Learning: Data Representations and Neural Networks
Log in to Mediaspace to watch this video
Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
Polynomial Regression: Overview
Log in to Mediaspace to watch this video
Covers polynomial regression, flexibility impact, and underfitting vs overfitting.
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.
Untitled
Log in to Mediaspace to watch this video
Feature Engineering: Polynomial Regression
Log in to Mediaspace to watch this video
Covers fitting linear regression on features of the original predictors for flexible feature representation.
Modern Regression: Statistical Models and Data Analysis
Log in to Mediaspace to watch this video
Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.
Design of Experiments: Response Surface
Log in to Mediaspace to watch this video
Explores experimental design methodology, including classic plans, simplex method, and canonical analysis for linear and quadratic models.
Previous
Page 2 of 3
Next
