Course home

Syllabus and Projects

 

 

---

---

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. Root Cause Localization with Data from AIOps Challenge: 65%

Syllabus:

Week	Date	Topic, Papers, Slides and Reading List	Algorithms & Techniques
1	Feb 21 (7:20 pm-9:45pm)	Course Introduction
2	Feb 28 (7:20 pm-9:45pm)	Video streaming	Data Visualization Correlation, Regression, Information gain, Decision trees, Regression trees
3	Mar 7 (7:20 pm-9:45pm)
4	Mar 14 (7:20 pm-9:45pm)
5	Mar 21 (7:20 pm-9:45pm)	KPI Anomaly detection	Time series Algorithms. Random Forests Deep Generative Models (VAE & GAN) Deep Sequence Learning Transformer
6	Mar 28 (7:20 pm-9:45pm)
7	Apr 4 (7:20 pm-9:45pm)	KPI Anomaly Localization	Similarity Clustering KDE Learning-to-Rank
8	Apr 11(7:20 pm-9:45pm)
9	Apr 18 (7:20 pm-9:45pm)	Log Anomaly Detection	Learning From Text SVM PCA Logistic Regression Transfer Learning
10	Apr 25 (7:20 pm-9:45pm)
11	May 2 (No class due to school holidays)
12	May 9 (7:20 pm-9:45pm)	Trace Anomaly Detection and Localization	Random Walk
13	May 16 (7:20 pm-9:45pm)	Incident Management	Multi-Instance-Learning Extreme Gradient Boosted Trees
14	May 23 (7:20 pm-9:45pm)	Resource Management	Deep Reinforcement Learning Graph Neural Network
15	May 30 (7:20 pm-9:45pm)	Signatureless Security	Neural Machine Translation
16	Jun 6 (7:20 pm-9:45pm)	Project Presentation