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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. System TroubleShooting: 15%

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

3. KPI Anomaly Detection 65%

Syllabus:

 

Week	Date	Topic, Papers, Slides and Reading List	Algorithms & Techniques
1	Feb 22 (7:20 pm-9:45pm)	Course Introduction
2	Mar 1 (7:20 pm-9:45pm)	Video streaming	Data Visualization Correlation, Regression, Information gain, Decision trees, Regression trees
3	Mar 8 (7:20 pm-9:45pm)
4	Mar 15 (7:20 pm-9:45pm)
5	Mar 22 (7:20 pm-9:45pm)	KPI Anomaly detection	Time series Algorithms. Random Forests Deep Generative Models (VAE & GAN) Deep Sequence Learning
6	Mar 29 (7:20 pm-9:45pm)
7	Apr 5 (University Holiday, No class)
8	Apr 12(7:20 pm-9:45pm)	KPI Anomaly Localization	Similarity Clustering KDE Learning-to-Rank
9	Apr 19 (7:20 pm-9:45pm)
10	Apr 26 (7:20 pm-9:45pm)	Log Anomaly Detection	Learning From Text SVM PCA Logistic Regression Transfer Learning
11	May 7th (Staturday) (7:20 pm-9:45pm)
12	May 10 (7:20 pm-9:45pm)	Trace Anomaly Detection and Localization	Random Walk
13	May 17 (7:20 pm-9:45pm)	Incident Management	Multi-Instance-Learning Extreme Gradient Boosted Trees
14	May 24 (7:20 pm-9:45pm)	Resource Management	Deep Reinforcement Learning Graph Neural Network
15	May 31 (7:20 pm-9:45pm)	Signatureless Security	Neural Machine Translation
16	Jun 7 (7:20 pm-9:45pm)	Project Presentation