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
Gaussian Mixture Regression: Theory and Applications
Graph Chatbot
Related lectures (54)
Statistical Learning: Fundamentals
Log in to Mediaspace to watch this video
Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.
Introduction to Machine Learning: Course Overview and Basics
Log in to Mediaspace to watch this video
Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.
Linear Regression: Fundamentals and Applications
Log in to Mediaspace to watch this video
Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.
Learning Sparse Features: Overfitting in Neural Networks
Log in to Mediaspace to watch this video
Discusses how learning sparse features can lead to overfitting in neural networks despite empirical evidence of generalization.
Machine Learning Fundamentals
Log in to Mediaspace to watch this video
Introduces machine learning basics, performance metrics, optimization techniques, and model evaluation.
Machine Learning at the Atomic Scale
Log in to Mediaspace to watch this video
Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.
Gradient Descent: Linear Regression
Log in to Mediaspace to watch this video
Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.
Overfitting in Supervised Learning: Case Studies and Techniques
Log in to Mediaspace to watch this video
Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.
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.
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.
Neural Networks Recap: Activation Functions
Log in to Mediaspace to watch this video
Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.
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.
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.
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.
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.
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.
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.
Edge Detection: Deep Learning Insights
Log in to Mediaspace to watch this video
Explores the evolution of edge detection techniques, from Canny to deep learning insights.
Understanding Data Attributes
Log in to Mediaspace to watch this video
Covers the analysis of various data attributes and linear regression models.
Machine Learning Applications: Regression and Classification
Log in to Mediaspace to watch this video
Explores machine learning applications in materials modeling, covering regression, classification, and feature selection.
Previous
Page 2 of 3
Next
