Все публикации

0:13:04

Clustering (Part 3): Hierarchical Clustering

0:19:13

Clustering (Part 2): Gaussian Mixture Models

0:16:14

Clustering (Part 1): Motivation and K-means Clustering

0:34:06

Deep learning (Part 2): Convolutional Neural Networks

0:24:24

Deep learning (Part 1): Motivation & Pre-training

0:15:09

Neural networks: Backpropagation Proof Outline

0:31:32

Basics on Multilayer Perceptron

0:18:16

Multi-class logistic regression

0:25:38

Logistic Regression (Part 2): Binary classification

0:20:24

Logistic Regression (Part 1): Maximum likelihood estimation, Bernoulli & Multinomial Distributions

0:04:52

Predicting the Effectiveness of Systematic Desensitization for Mitigating Public Speaking Anxiety

0:14:58

Bio-behavioral signal trajectories of state anxiety during public speaking

0:29:28

Non-linear regression, overfitting, regularization

0:21:54

Linear Regression (Part 2): Approximate solution via Gradient Descent

0:25:48

Linear regression (Part 1): Example, Representation, Numerical Solution

0:04:27

Linear Perceptron (Part 1): Vector perpendicular to line

0:32:55

Linear perceptron algorithm

0:06:27

K-Nearest Neighbor (Part 3): Variations

0:22:37

K-Nearest Neighbor (Part 2): Practical use & hyperparameter tuning

0:26:49

K-Nearest Neighbor (Part 1): Representation & Basics

0:44:34

Introduction to Machine Learning

0:21:47

Machine Learning Linear Algebra Practice Problem (Part 1)

0:11:25

Machine Learning Linear Algebra Practice Problem (Part 2)

0:06:54

Common challenges in machine learning