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Syllabus and Projects

 

 

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Assignments (Each student finishes each assignment alone)

1. Data processing and visualization: 10%

2. Log Anomaly Detection: 20%

Project (A team of 2-3 students finish the project together)

3. Microservices Anomaly Detection and Localization Competition from  AIOps Challenge: 60%

Syllabus:

 

Week	Date	Topic, Papers, Slides and Reading List	Algorithms & Techniques
1	Sep 16 (19:20-21:45)	Course Introduction
2	Sep 23 (19:20-21:45)	Video streaming	Data Visualization Correlation, Regression, Information gain, Decision trees, Regression trees
3	Sep 30 (19:20-21:45)
4	Oct 7 (19:20-21:45)
5	Oct 14 (19:20-21:45)	KPI Anomaly detection	Time series Algorithms. Random Forests Deep Generative Models (VAE & GAN) Deep Sequence Learning
6	Oct 21 (19:20-21:45)
7	Oct 28 (19:20-21:45)	KPI Anomaly Localization	Similarity Clustering KDE Learning-to-Rank
8	Nov 4 (19:20-21:45)
9	Nov 11 (19:20-21:45)	Log Anomaly Detection	Learning From Text SVM PCA Logistic Regression Transfer Learning
10	Nov 18 (19:20-21:45)
11	Nov 25 (19:20-21:45)	Trace Anomaly Detection and Localization	Random Walk
12	Dec 2 (19:20-21:45)	Incident Management	Multi-Instance-Learning Extreme Gradient Boosted Trees
13	Dec 9 (19:20-21:45)	Resource Management	Deep Reinforcement Learning Graph Neural Network
14	Dec 16 (19:20-21:45)	Signatureless Security	Neural Machine Translation
15	Dec 23 (19:20-21:45)	Project Presentation