08: Industrie 4.0, Vorlesung, WS 2017/18, 22.12.2017
Author
Editor
Participating institute
Institut für Anthropomatik und Robotik (IAR)
Genre
Description
- 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
Duration (hh:mm:ss)
01:22:08
Series
Industrie 4.0, Vorlesung, WS 2017/18
Published on
22.12.2017
Subject area
License
| Resolution | 1280 x 720 Pixel |
| Aspect ratio | 16:9 |
| Audio bitrate | 127715 bps |
| Audio channels | 2 |
| Audio Codec | aac |
| Audio Sample Rate | 48000 Hz |
| Total Bitrate | 933875 bps |
| Color Space | yuv420p |
| Container | mov,mp4,m4a,3gp,3g2,mj2 |
| Media Type | video/mp4 |
| Duration | 4928 s |
| Filename | DIVA-2017-843_hd.mp4 |
| File Size | 4.096 byte |
| Frame Rate | 25 |
| Video Bitrate | 800068 bps |
| Video Codec | h264 |
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Industrie 4.0, Vorlesung, WS 2017/18
Episodes 1-12
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