Mathematical Data Science 1 2020 /KursID:1234
- Letzter Beitrag vom 2020-07-22
Schlüsselworte: Perceptron backpropagation classification deep learning Gradient descent Neural Network Data Science Training data Support Vector Machine Hyperplane Supervised learning

Lehrende(r)

Dr. Daniel Tenbrinck

Einrichtung

Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)

Aufzeichnungsart

Vorlesungsreihe

Zugang

Frei / IdM-Anmeldung

Sprache

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1
Clustering - Intro preview image
Clustering - Intro
Dr. Philipp Wacker
2020-04-27
IdM-Anmeldung
00:06:15
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1
Introduction to Data Science preview image
Introduction to Data Science
Dr. Daniel Tenbrinck
2020-04-13
Frei
00:36:40
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2
Clustering - K-Means preview image
Clustering - K-Means
Dr. Philipp Wacker
2020-04-27
IdM-Anmeldung
00:22:06
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3
Clustering - Gaussian Mixture Model preview image
Clustering - Gaussian Mixture Model
Dr. Philipp Wacker
2020-04-27
IdM-Anmeldung
00:43:07
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4
Clustering - The general EM algorithm preview image
Clustering - The general EM algorithm
Dr. Philipp Wacker
2020-04-27
IdM-Anmeldung
00:19:29
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5
Clustering - The EM algorithm for Gaussian Mixture Models preview image
Clustering - The EM algorithm for Gaussian Mixture Models
Dr. Philipp Wacker
2020-04-27
IdM-Anmeldung
00:16:49
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6
Clustering - Some comments on EM and K-means preview image
Clustering - Some comments on EM and K-means
Dr. Philipp Wacker
2020-04-27
IdM-Anmeldung
00:13:05
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7
(Linear) Principal Component Analysis preview image
(Linear) Principal Component Analysis
Dr. Daniel Tenbrinck
2020-05-04
Frei
00:39:11
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8
Kernel Principal Component Analysis preview image
Kernel Principal Component Analysis
Dr. Daniel Tenbrinck
2020-05-11
Frei
00:44:37
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9
Random variables preview image
Random variables
Dr. Philipp Wacker
2020-05-14
Frei
00:27:56
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10
Nonsingular Bayes' Law preview image
Nonsingular Bayes' Law
Dr. Philipp Wacker
2020-05-14
Frei
00:17:55
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11
Bayes law for random variables preview image
Bayes law for random variables
Dr. Philipp Wacker
2020-05-14
Frei
00:15:30
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12
Bayesian Inverse Problems preview image
Bayesian Inverse Problems
Dr. Philipp Wacker
2020-05-14
Frei
00:35:03
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13
Monte Carlo integration preview image
Monte Carlo integration
Dr. Philipp Wacker
2020-05-14
Frei
00:22:38
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14
Obtaining samples directly preview image
Obtaining samples directly
Dr. Philipp Wacker
2020-05-14
Frei
00:13:07
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15
Importance Sampling preview image
Importance Sampling
Dr. Philipp Wacker
2020-05-14
Frei
00:18:15
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16
Rejection Sampling preview image
Rejection Sampling
Dr. Philipp Wacker
2020-05-14
Frei
00:14:42
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17
Markov chains preview image
Markov chains
Dr. Philipp Wacker
2020-05-14
Frei
00:29:26
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18
Convergence of Markov chains preview image
Convergence of Markov chains
Dr. Philipp Wacker
2020-05-14
Frei
00:05:49
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19
Metropolis--Hastings preview image
Metropolis--Hastings
Dr. Philipp Wacker
2020-05-14
Frei
00:20:03
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20
Correctness of Metropolis--Hastings preview image
Correctness of Metropolis--Hastings
Dr. Philipp Wacker
2020-05-14
Frei
00:14:21
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21
Introduction to Machine Learning preview image
Introduction to Machine Learning
Dr. Daniel Tenbrinck
2020-06-07
Frei
00:38:25
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22
Artificial Neural Networks in a Nutshell preview image
Artificial Neural Networks in a Nutshell
Dr. Daniel Tenbrinck
2020-06-08
Frei
00:40:58
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23
Training Artificial Neural Networks using Backpropagation preview image
Training Artificial Neural Networks using Backpropagation
Dr. Daniel Tenbrinck
2020-06-15
Frei
00:49:03
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24
Optimisation in Training Artificial Neural Networks preview image
Optimisation in Training Artificial Neural Networks
Dr. Daniel Tenbrinck
2020-06-19
Frei
00:23:42
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27
Student talk: Linear Support Vector Machines preview image
Student talk: Linear Support Vector Machines
Dr. Daniel Tenbrinck
2020-07-22
Frei
00:37:37
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