- Docente: Cinzia Viroli
- Credits: 3
- SSD: SECS-S/01
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Statistical Sciences (cod. 9222)
Learning outcomes
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).
Course contents
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
Readings/Bibliography
Lecture notes
Teaching methods
Theoretical and practical lessons in labs
Assessment methods
Compulsory attendance to the course
Office hours
See the website of Cinzia Viroli