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
Comparing L1 and L0 + Greedy algorithms
Graph Chatbot
Related lectures (48)
Regularization in Machine Learning
Log in to Mediaspace to watch this video
Introduces regularization techniques to prevent overfitting in machine learning models.
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.
Kernel Methods: Understanding Overfitting and Model Selection
Log in to Mediaspace to watch this video
Discusses kernel methods, focusing on overfitting, model selection, and kernel functions in machine learning.
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.
Kernel Methods in Machine Learning: Kernel Regression and SVM
Log in to Mediaspace to watch this video
Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.
Polynomial Regression and Gradient Descent
Log in to Mediaspace to watch this video
Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.
Regression Again: Exercise 3.1
Log in to Mediaspace to watch this video
Covers exercises on regression, including linear and polynomial regression, high dimensions, and real data analysis.
Understanding Data Attributes
Log in to Mediaspace to watch this video
Covers the analysis of various data attributes and linear regression models.
Regression: Interactive Lecture
Log in to Mediaspace to watch this video
Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.
Untitled
Log in to Mediaspace to watch this video
Regression Methods: Model Building and Inference
Log in to Mediaspace to watch this video
Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.
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.
Feature Engineering: Missing Data and Standardization
Log in to Mediaspace to watch this video
Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.
Model Selection and Evaluation: Bias-Variance Dilemma, Ridge Estimation
Log in to Mediaspace to watch this video
Explores over-learning, generalization, and under-learning in machine learning models.
Binary Classification by Regression: Decision Functions and Cost Functions
Log in to Mediaspace to watch this video
Explores binary classification by regression, decision functions, and various cost functions.
Decision Trees: Regression and Classification
Log in to Mediaspace to watch this video
Covers decision trees for regression and classification, explaining tree construction, feature selection, and criteria for induction.
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.
Machine Learning: Supervised and Unsupervised Learning Techniques
Log in to Mediaspace to watch this video
Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Gaussian Mixture Regression: Theory and Applications
Log in to Mediaspace to watch this video
Explores Gaussian mixture regression and overfitting with multiple Gauss functions.
Machine Learning Fundamentals: Structure Discovery, Classification, Regression
Log in to Mediaspace to watch this video
Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.
Previous
Page 2 of 3
Next
