Machine Learning | Video Online

Model Selection and Estimation of the Generalization Cost (cont.) // Bayesian Networks: Construction, Inference, Learning and Causal Interpretation // Deep X: Deep Learning with Deep Knowledge

(00:00:00) >: Introduction and recapitulation

(00:36:55) >: A: Classical Frequentist Approaches: Akaikes Information Criterion (AIC)

(00:42:17) >: B: Bayesian Approaches

(00:56:43) >: C: Modern Frequentist Approaches

(01:10:39) >: Bayesian Networks: Construction, Inference, LEarning and Causal Interpretation

(01:25:21) >: Deep X: Deep Learning with Deep Knowledge

Recording from: 05.07.2018

Lecturer: Volker, Tresp

Model Selection and Estimation of the Generalization Cost

(00:00:00) >: Introduction and recapitulation

(01:02:00) >: Model Selection and Estimation of the Generalization Cost

(01:23:07) >: Learning Theories

(01:36:02) >: A: Classical Frequentist Approach

Recording from: 28.06.2018

Lecturer: Volker, Tresp

Frequentist Statistics and Bayesian Statistics (cont.) // Linear Classification // Optimal Separating Hyperplane and the Support Vector Machine

(00:00:00) >: Introduction and recapitulation

(00:34:54) >: Bayesian Statistics

(00:47:19) >: Linear Classification

(01:29:53) >: Logistic Regression in Medical Statistics

(01:42:38) >: Optimal Separating Hyperplane and the Support Vector Machine

Recording from: 21.06.2018

Lecturer: Volker, Tresp

Factorization, Principal Component Analysis and Singular Value Decomposition

(00:00:00) >: Introduction and recapitulation: Linear Regression and Unknown Inputs

(00:07:43) >: Factorization

(00:18:20) >: PCA

(00:38:42) >: PCA Applications

(00:42:52) >: PCA Example: Handwritten Digits

(00:49:32) >: Eigenfaces: similarity search of images

(00:57:50) >: PCA and Singular Value Decomposition

(01:09:43) >: Application: Similarities Between Documents

Recording from: 14.06.2018

Lecturer: Florian Buettner

Some Concepts of Probability (Review) // Frequentist Statistics and Bayesian Statistics

(00:00:00) >: Introduction and recapitulation

(00:33:55) >: Some Concepts of Probability (Review)

(01:31:31) >: Frequentist Statistics

(02:06:55) >: Bayesian Statistics

Recording from: 07.06.2018

Lecturer: Volker, Tresp

Deep Learning

(00:00:00) >: Introduction and Overview

(00:04:36) >: Introduction to Deep Learning

(00:30:27) >: Implementation of a Deep Learning Model

(00:38:41) >: Basic Building Blocks

(01:06:04) >: Thinking in Macro Structures

(01:15:28) >: End-to-End Model Design

(01:19:27) >: Deep Learning Model Training

(01:25:32) >: Loss Function Design

(01:33:37) >: Optimization

(01:51:28) >: Regularization

(01:53:12) >: Additional Notes

Recording from: 24.05.2018

Lecturer: Dr. Denis Krompaß

Feature Spaces, Manifolds, and Deep Generative Models (cont.) // Kernels

(00:00:00) >: Introduction and recapitulation

(00:14:29) >: Feature Spaces, Manifolds, and Deep Generative Models (cont.)

(00:58:41) >: Kernels

Recording from: 17.05.2018

Lecturer: Volker, Tresp

Deep Learning // Feature Spaces, Manifolds, and Deep Generative Models

(00:00:00) >: Introduction and recapitulation

(00:37:10) >: Deep Learning

(01:14:03) >: Convolutional Neural Networks (CNNs)

(01:40:08) >: Feature Spaces, Manifolds, and Deep Generative Models

Recording from: 03.05.2018

Lecturer: Volker, Tresp

Basis Functions // Neural Networks

(00:00:00) >: Introduction and recapitulation

(00:27:54) >: Basis Functions

(01:17:43) >: Neural Networks

Recording from: 26.04.2018

Lecturer: Volker, Tresp

The Perceptron // Linear Algebra (Review) // Linear Regression

(00:00:00) >: Introduction and recapitulation

(00:31:09) >: The Perceptron

(01:23:16) >: Linear Algebra (Review)

(01:34:41) >: Linear Regression

Recording from: 19.04.2018

Lecturer: Volker, Tresp

Introduction

(00:00:00) >: Introduction

(00:21:06) >: Non-Technical Perspectives on Learning: Philosophy

(00:50:56) >: Non-Technical Perspectives on Learning: Psychology, Cognition and Cognitive Neuroscience

(01:17:14) >: Non-Technical Perspectives on Learning: Cellular Neuroscience

(01:26:18) >: Machine Learning: Before the Computer Age to Today: Statistics

(01:35:56) >: Machine Learning: Neural Computation I (Perceptron)

(01:41:10) >: Machine Learning: Classical Artificial Intelligence

(01:50:34) >: Machine Learning: Neural Computation II (Multilayer Perception)

(01:56:20) >: Machine Learning: Mathematically Wee-Founded Models

(01:57:34) >: Machine Learning: Neural Computation III (Deep Learning

(02:04:31) >: Details on the Lecture

Recording from: 12.04.2018

Lecturer: Volker, Tresp

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