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Big Data Science and Machine Learning
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Big Data Science and Machine Learning
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Academic year 2022/2023
- Teachers
- Federica Legger (Lecturer)
Dr. Gabriele Gaetano Fronze' (Lecturer) - Degree course
- PhD in Physics
- Teaching period
- First semester
- Type
- Elective
- Credits/Recognition
- 4
- Course disciplinary sector (SSD)
- FIS/01 - experimental physics
- Delivery
- Traditional
- Language
- English
- Attendance
- Obligatory
- Type of examination
- Practice test
- Prerequisites
- Basic knowledge of python and C++ is required
In particular I suggest to get familiar with Jupyter notebooks, numpy and pandas before the course starts. No expert knowledge is required, but doing a couple of tutorials on these topics (easily found on the web) is highly recommended.
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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- Oggetto:
Course delivery
The course will be held in person, and it will not be possible to attend remotely. To pass the course you need to follow 80% of the lessons and pass the final test.
Schedule:
- June 12th-16th, for two hours/day, 10-12 - Aula Wick
- 1 full-day course on June 20th (10-13 and 14:30-17:30) - Aula Avogadro
- The exam will be on June 22nd (but can be turned in up to June 30th)
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Learning assessment methods
Practical test
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!
- Enroll
- Closed
- Enrollment opening date
- 18/11/2022 at 00:00
- Enrollment closing date
- 01/03/2023 at 00:00
- Maximum number of students
- 15 (Once this number of students is reached, enrollment will no longer be permitted!)
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