AIOps Fall2019 – Syllabus

 

 



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. Time Series Anomaly Detection  Competition from  AIOps Challenge: 60%

Syllabus:

Week Date Topic, Papers, Slides and Reading List Algorithms & Techniques
1 Sep 11 (9:50-12:15) Course Introduction
2 Sep 18 (9:50-12:15) Video streaming Data Visualization

Correlation, Regression, Information gain, Decision trees, Regression trees

 

3 Sep 25 (9:50-12:15)
4 Oct 2 (No Class due to school holidays)
5 Oct 9 (No Class )
6 Oct 16 (9:50-12:15) KPI Anomaly detection Time series Algorithms.

Random Forests

Deep Generative Models (VAE & GAN)

Clustering

Similarity

7 Oct 23 (9:50-12:15)
8 Oct 30 (9:50-12:15) Log Anomaly Detection Learning From Text

CNN

Deep Sequence Learning

9 Nov 6 (9:50-12:15)
10 Nov 13 (9:50-12:15)

Anomaly Localization

 

Monte Carlo Tree Search

11 Nov 20 (9:50-12:15) Event/Failure Prediction Regularization

SVM

Transfer Learning

Extreme Gradient Boosted Trees

12 Nov 27 (9:50-12:15) Resource Management Deep Reinforcement Learning
13 Dec 4 (9:50-12:15) Operations Knowledge Graph Association Mining

 Causal Inference

Knowledge Graph

14 Dec 11 (9:50-12:15)
15 Dec 18 (9:50-12:15)
16 Dec 25 (9:50-12:15) Project Presentation

 

 
 
 
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