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 finish the project together)

3. Microservices Anomaly Detection and Localization Competition from  AIOps Challenge: 65%

Syllabus:

 

Week	Date	Topic, Papers, Slides and Reading List	Algorithms & Techniques
1	Feb 24 (9:50am-12:15pm)	Course Introduction
2	Mar 3 (9:50am-12:15pm)	Video streaming	Data Visualization Correlation, Regression, Information gain, Decision trees, Regression trees
3	Mar 10 (9:50am-12:15pm)
4	Mar 17 (9:50am-12:15pm)
5	Mar 24 (9:50am-12:15pm)	KPI Anomaly detection	Time series Algorithms. Random Forests Deep Generative Models (VAE & GAN) Deep Sequence Learning
6	Mar 31 (9:50am-12:15pm)
7	Apr 7 (9:50am-12:15pm)	KPI Anomaly Localization	Similarity Clustering KDE Learning-to-Rank
8	Apr 14 (9:50am-12:15pm)
9	Apr 21 (9:50am-12:15pm)	Log Anomaly Detection	Learning From Text SVM PCA Logistic Regression Transfer Learning
10	Apr 28 (9:50am-12:15pm)
11	May 5 (No Class due to school holidays)
12	May 12 (9:50am-12:15pm)	Trace Anomaly Detection and Localization	Random Walk
13	May 19 (9:50am-12:15pm)	Incident Management	Multi-Instance-Learning Extreme Gradient Boosted Trees
14	May 26 (9:50am-12:15pm)	Resource Management	Deep Reinforcement Learning Graph Neural Network
15	Jun 2 (9:50am-12:15pm)	Signatureless Security	Neural Machine Translation
16	Jun 9 (9:50am-12:15pm)	Project Presentation