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Oggetto:

Big Data Science and Machine Learning

Oggetto:

Big Data Science and Machine Learning

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Academic year 2021/2022

Teacher
Federica Legger
Degree course
PhD in Physics
Teaching period
First semester
Type
Elective
Credits/Recognition
4
Course disciplinary sector (SSD)
FIS/01 - experimental physics
Delivery
Formal authority
Language
English
Attendance
Obligatory
Type of examination
Practice test
Prerequisites
Basic knowledge of python and C++
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Sommario del corso

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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.

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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

Suggested readings and bibliography

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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



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Notes

Students wishing to take this course must register!

Period

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

Enroll
  • Closed
    Enrollment opening date
    01/02/2022 at 00:00
    Enrollment closing date
    01/05/2022 at 00:00
    Maximum number of students
    15 (Once this number of students is reached, enrollment will no longer be permitted!)
    Oggetto:
    Last update: 25/10/2022 17:25
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