94451 - SEM. MACHINE LEARNING AND BIG DATA IN R: THEORY AND APPLICATIONS

Anno Accademico 2019/2020

  • Docente: Cinzia Viroli
  • Crediti formativi: 3
  • SSD: SECS-S/01
  • Lingua di insegnamento: Inglese
  • Modalità didattica: Convenzionale - Lezioni in presenza
  • Campus: Bologna
  • Corso: Laurea Magistrale in Statistical sciences (cod. 9222)

Conoscenze e abilità da conseguire

The students will acquire theoretical knowledge of data analysis, machine learning, and other computational methods. They will be able to implement such algorithms on their own in R. Outline: Day 1: Run-time measurement and estimation, Code profilling, Integration of R with C++. Day 2: Theoretical background of parallel processing, Approaches to parallelization, Load balancing. Day 3: Large memory and out-of-memory data, Efficient Computing from RAM, Computing from Efficient File Structures. Day 4: Applications of machine learning algorithms (k-nearest neighbors, classification and regression trees). Day 5: Applications of machine learning algorithms (artificial neural networks).

Contenuti

This course will be taught by Dr. Krzysztof Gajowniczek (krzysztof_gajowniczek@sggw.pl) of the Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences.

 

Timetable

  • April 20 13-16 lab T
  • April 21 16-19 lab G
  • April 22 11-14 lab G
  • April 23 9-11 lab G
  • April 23 16-18 lab G
  • April 24 9-11 lab G

Testi/Bibliografia

Lecture notes

Metodi didattici

Theoretical and practical lessons in labs

Modalità di verifica e valutazione dell'apprendimento

Compulsory attendance to the course

Orario di ricevimento

Consulta il sito web di Cinzia Viroli