09: Industrie 4.0, Vorlesung, WS 2018/19, 14.12.2018
Author
Editor
Participating institute
Institut für Anthropomatik und Robotik (IAR)
Genre
Description
- 0:00:00 Start
- 0:00:38 Graphic Displays of Basic Statistical Descriptions
- 0:05:29 Histogram Analysis
- 0:07:18 Histograms Often Tell More than Boxplots
- 0:08:08 Positively and Negatively Correlated Data
- 0:09:29 Data Visualization
- 0:11:57 PIxel-oriented Visualization Techniques
- 0:13:19 Geometric Projection Visualization Techniques
- 0:15:46 Similarity and Dissimilarity
- 0:17:38 Major Tasks in Data Preprocessing
- 0:21:35 Data Cleaning
- 0:24:41 Incomplete (Missing) Data
- 0:27:15 How to handle Noisy Data?
- 0:30:59 Example Use Case: Predictive Maintenance
- 0:40:50 Personal Lessons Learned
- 0:48:29 Q Learning – Example: Atari Games
- 0:54:54 Machine Learning
- 1:00:10 Example Use Cases: ML in Industry 4.0
- 1:12:54 Supervised Machine Learning Algorithms
- 1:18:18 Unsupervised Machine Learning Algorithms
- 1:25:24 Labeled Data und Unlabeled Data
Duration (hh:mm:ss)
01:27:13
Series
Industrie 4.0, Vorlesung, WS 2018/19
Published on
14.12.2018
Subject area
License
| Resolution | 1280 x 720 Pixel |
| Aspect ratio | 16:9 |
| Audio bitrate | 128000 bps |
| Audio channels | 2 |
| Audio Codec | aac |
| Audio Sample Rate | 48000 Hz |
| Total Bitrate | 934084 bps |
| Color Space | yuv420p |
| Container | mov,mp4,m4a,3gp,3g2,mj2 |
| Media Type | video/mp4 |
| Duration | 5233 s |
| Filename | DIVA-2018-977_hd.mp4 |
| File Size | 611.060.746 byte |
| Frame Rate | 25 |
| Video Bitrate | 799986 bps |
| Video Codec | h264 |
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Industrie 4.0, Vorlesung, WS 2018/19
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