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)
Feature Selection, Kernel Regression, Neural Networks Playground
Log in to Mediaspace to watch this video
Covers feature selection, kernel regression, and neural networks through exercises.
Regression: Exercises
Log in to Mediaspace to watch this video
Covers exercises on regression functions using RLS, WLS, and LWR.
Untitled
Log in to Mediaspace to watch this video
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.
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 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.
Neural Networks: Perceptron and Backpropagation
Log in to Mediaspace to watch this video
Covers the basics of neural networks, including the perceptron model and backpropagation.
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
Log in to Mediaspace to watch this video
Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.
Deep Learning: Principles and Applications
Log in to Mediaspace to watch this video
Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.
Model Evaluation
Log in to Mediaspace to watch this video
Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
Data-Driven Modeling in Neuroscience: Meenakshi Khosla
Log in to Mediaspace to watch this video
By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Nonlinear Regression: Solutions to Exercises
Log in to Mediaspace to watch this video
Explores nonlinear regression using Gaussian components for density modeling.
Receiver-Operator Characteristics: ROC Curves
Log in to Mediaspace to watch this video
Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.
Previous
Page 3 of 3
Next