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08: Industrie 4.0, Vorlesung, WS 2017/18, 22.12.2017
Autor
Herausgeber
Beteiligtes Institut
Institut für Anthropomatik und Robotik (IAR)
Genre
Beschreibung
- 0:00:00 Starten
- 0:02:01 Positively and Negatively Correlated Data
- 0:03:13 Data Visualization
- 0:05:00 Pixel-Oriented Visualization Techniques
- 0:06:22 Geometric Projection Visualization Techniques
- 0:08:21 Similarity and Dissimilarity
- 0:10:21 Data Quality: Why Preprocess the Data?
- 0:13:46 Major Tasks in Data Preprocessing
- 0:17:39 Data Cleaning
- 0:20:00 How to handle Missing Data
- 0:21:18 Noisy Data
- 0:27:40 Example Use Case: Predictive Maintenance
- 0:29:35 Machine Learning
- 0:36:44 Personal Lessons Learned
- 0:40:29 Q Learning - Example: Atari Games
- 0:43:09 Back to Machine Learning
- 0:45:49 Example Use Cases: ML in Industry 4.0
- 0:52:15 Supervised Machine Learning Algorithmus
- 0:57:58 Unsupervised Machine Learning Algorithmus
- 0:59:59 Reinforcement Machine Learning Algorithmus
- 1:06:18 Labeled and Unlabeled Data
- 1:07:53 Typical Machine Learning Process
- 1:11:28 Introduction to ML
- 1:13:19 Common ML Algorithmus in Industry 4.0
- 1:14:27 Naive Bayes Classifier Algorithm
- 1:16:45 k-means Clustering Algorithm
- 1:18:50 Linear Regression Machine Learning Algorithm
Laufzeit (hh:mm:ss)
01:22:08
Serie
Industrie 4.0, Vorlesung, WS 2017/18
Publiziert am
22.12.2017
Fachgebiet
Lizenz
| Auflösung | 1280 x 720 Pixel |
| Seitenverhältnis | 16:9 |
| Audiobitrate | 127715 bps |
| Audio Kanäle | 2 |
| Audio Codec | aac |
| Audio Abtastrate | 48000 Hz |
| Gesamtbitrate | 933875 bps |
| Farbraum | yuv420p |
| Container | mov,mp4,m4a,3gp,3g2,mj2 |
| Medientyp | video/mp4 |
| Dauer | 4928 s |
| Dateiname | DIVA-2017-843_hd.mp4 |
| Dateigröße | 4.096 byte |
| Bildwiederholfrequenz | 25 |
| Videobitrate | 800068 bps |
| Video Codec | h264 |
Embed-Code
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