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
Logistic Regression: Probability Modeling and Optimization
Graph Chatbot
Related lectures (49)
Introduction to Inference
Log in to Mediaspace to watch this video
Covers the basics of probability theory, random variables, joint probability, and inference.
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 Classification: Logistic Regression
Log in to Mediaspace to watch this video
Covers linear classification using logistic regression, regularization, and multiclass classification.
Probability and Statistics: Basics and Applications
Log in to Mediaspace to watch this video
Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.
Gaussian Mixture Models & Noisy Signals
Log in to Mediaspace to watch this video
Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.
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.
Conditional Probability: Understanding Events and Their Relationships
Log in to Mediaspace to watch this video
Covers conditional probability, illustrating how events influence each other's probabilities and its applications in real-world scenarios.
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.
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.
Deep Learning: Multilayer Perceptron and Training
Log in to Mediaspace to watch this video
Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.
Expectation Maximization: Learning Parameters
Log in to Mediaspace to watch this video
Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.
Elements of Statistics: Memorylessness, Stationary Processes, Estimation using MLE
Log in to Mediaspace to watch this video
Explores memorylessness in distributions, stationary processes, and estimation using MLE.
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.
Statistical Theory: Maximum Likelihood Estimation
Log in to Mediaspace to watch this video
Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.
Normal Distribution: Properties and Calculations
Log in to Mediaspace to watch this video
Covers the normal distribution, including its properties and calculations.
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.
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.
Elements of Statistics: Probability and Random Variables
Log in to Mediaspace to watch this video
Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.
Clustering & Density Estimation
Log in to Mediaspace to watch this video
Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.
Data Analysis Techniques: Amplitude Shift Keying and Graphical Methods
Log in to Mediaspace to watch this video
Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.
Previous
Page 2 of 3
Next