Phd in Physics

Big Data Science and Machine Learning

11- Big Data Science and Machine Learning

Academic year 2021/2022

Sommario del corso

• Course objectives
• Program
• Suggested textbooks and readings
• Notes
• Teaching tools

Course objectives

Data science is one of the fastest growing fields of information technology, with wide applications in key sectors such as research, industry, public administration. The course will cover the definition of big data and the basic techniques to store, handle and process them. Machine Learning (ML) and Deep Learning (DL) algorithms will be briefly introduced. We will focus on the technical implementation of different ML algorithms, focusing on the parallelisation aspects and the deployment on distributed resources and different architectures (CPUs, FPGAs, GPUs). A basic introduction to the current computer architecture will be given, with a focus on parallel computing paradigms aimed at the exploitation of the full potential of parallel architectures. An overview of the fundamental OpenMP and MPI coding patterns is covered during hands-on sessions.

Program

- Introduction to big data science
- The big data pipeline: state-of-the-art tools and technologies
- ML and DL methods: supervised and unsupervised training, neural network models
- Introduction to computer architecture and parallel computing patterns
- Initiation to OpenMP and MPI
- Parallelisation of ML algorithms on distributed resources
- Beyond CPUs: ML applications on distributed architectures, GPUs, FPGAs

Chen, M., Mao, S. & Liu, Y. Mobile Netw Appl (2014) 19: 171. https://doi.org/10.1007/s11036-013-0489-0

Yao, Yuanshun & Xiao, Zhujun & Wang, Bolun & Viswanath, Bimal & Zheng, Haitao & Y. Zhao, Ben. (2017). Complexity vs. performance: empirical analysis of machine learning as a service. 384-397. 10.1145/3131365.3131372

Note

Students wishing to take this course must register!

Period

2-12 May 2022, 10:00-12:00

• Teaching materials
• Exams and finals
• Go to Moodle
• Visit the forums