EE769-9-1 Combining Models - Introduction and Hypothesis Spaces

(20:37)

EE769-9-2 Combining Models - Ensembles

(21:40)

EE769-9-5 Combining Models - Boosting

(13:53)

EE769-9-3-1 Combining Models - Decision Trees 1

(19:28)

EE769-9-3-2 Combining Models - Decision Trees 2

(14:33)

EE769 2 9 Basic Math for ML - Entropy, and joint, conditional, marginal distributions

(15:15)

Overhyped Physicists: Richard Feynman

(12:22)

Lecture 13 : Probability Density Estimation - IV

(56:40)

Statistical Machine Learning Part 26 - Kernel PCA

(54:28)

Lecture 07 - Seismic Hazard Analysis

(34:30)

16. Learning: Support Vector Machines

(49:34)

Derive the Dual Formulation for Support Vector Machines [Lecture 3.3]

(23:41)

Ali Ghodsi, Deep Learning, Attention mechanism, self-attention, S2S, Fall 2023, Lecture 9

(1:17:52)

Kernel density estimation (Excel)

(13:58)

10.4 Non-Linear Principal Component Analysis (UvA - Machine Learning 1 - 2020)

(21:49)

Cluster validation: Silhouette score, Davies-Bouldin score, Calinski-Harabasz score - [Python]

(14:53)

EE769-9-4 Combining Models - Model randomization and random forests

(9:6)

EE769-7-1-4 Neural Networks - Layers and their function

(21:28)

EE769 1 1 Introduction to Machine Learning

(58:29)

Lecture 9 | Machine Learning (Stanford)

(1:14:19)

ML Project Presentation EE769

(14:54)

EE769 1 2 2 ML for Smart Monkeys

(34:37)