# ~ Home ~

## Description

This course aims to provide students an understanding in the operating principles and hands-on experience with mainstream Big Data Computing systems. Open-source platforms for Big Data processing and analytics would be discussed. Data mining algorithms and machine learning applications are another major stream of this course. In addition, widely-adopted optimization methods and models for big data analytics will also be investigated. Topics to be covered include:

• Programming models and design patterns for mainstream Big Data computational frameworks ;

• System Architecture and Resource Management for Data-center-scale Computing ;

• Algorithm Design for Big Data Analytics, e.g., SVM Model, K-means Clustering, Deep Neural Network ;

• Optimization Methods, e.g., convex optimization, gradient descent, online optimization ;

## Course Pre-requisite:

This course contains substantial hands-on components which require solid background in programming and hands-on operating systems experience. If you have never used a command-line interface to install/configure/manage an operating system, e.g. a linux-based one, you will need to pick-up the skills yourself and IT CAN BE VERY TIME-CONSUMING for you to complete the homeworks. (Students without the aforementioned required background may take several 10's of hours to finish EACH homework assignment).

## Course Information

Lecture time and venue:

• 6E205 ; Wed 7:30pm - 10:15pm

Instructor:

• Prof. Huanle Xu. xuhl [at] dgut [dot] edu [dot] cn
• Office hours: Fri 4:30-5:15pm or by Appointment (9A 304)

Teaching Assistant:

• Chuda Xiao

## Recommended Programming References

• [DataAlgorithms] Data Algorithms: Recipes for Scaling Up with Hadoop and Spark, by Mahmoud Parsian, Publisher: O'Reilly Media, Aug 2015