Advanced Programming in Python (2235.YR.014755.1)
General information
Type: |
OPT |
Curs: |
1 |
Period: |
S semester |
ECTS Credits: |
3 ECTS |
Teaching Staff:
Group |
Teacher |
Department |
Language |
Year 1 |
Manuel Guerris Larruy |
Operaciones, Innovación y Data Sciences |
ENG |
Prerequisites
Basic knowledge of python or have cursed an optative like Introduction to Programming with Python.
Previous Knowledge
This course builds on basic python knowledge, having good python programming skills is a plus.
Workload distribution
The course consist on four modules: Advanced python programming, introduction to neural networks, Introduction to PyTorch and Deep learning with PyTorch. These four modules will be distributed along the duration of the course.
COURSE CONTRIBUTION TO PROGRAM
This course enables you to do advanced data analysis using python.
Course Learning Objectives
Nowadays, and thanks to the big advancements in technology, companies are often required to ingest and process big quantities of data. In this regard, companies have departed from the traditional way of using spreadsheets, to more advanced data processing strategies and big data analysis tools, which often are based on deep learning and complex data analysis programs.
Given the needs that nowadays companies require for data analysis, having advanced knowledge of programming strategies and basic notions of deep learning is a fundamental skill.
This course enables you to achieve advanced programming skills in python, using production ready tools, as well as having a better understanding of the inner workings of deep learning in python.
We cannot promise that you will become an expert python programmer, but surely you will develop the skills to become one.
Looking forward to meet you!!
Methodology
This course will be thought combining lectures, live code sessions with exercises performed in class, open debates regarding programming strategies and and a final project.
We will mostly use python and PyTorch.
Assessment criteria
The assessment criteria is split between:
Active participation (60 %): of which 20 % is attendance and 40 % is team-collaboration
Final project (40 %)
Bibliography
Howard J., Gugger S., (2020). Deep Learning for Coders with fastai and PyTorch. O'Reilly Media, Inc, ISBN: 9781492045526
Goodfellow I., Bengio Y., Courville A., (2016). Deep Learning, Massachusetts Institute of Tecnhology, ISBN: 9780262035613
Timetable and sections
Group |
Teacher |
Department |
Year 1 |
Manuel Guerris Larruy |
Operaciones, Innovación y Data Sciences |
Timetable Year 1
From 2024/4/29 to 2024/5/17:
Monday and Friday from 8:00 to 9:30. (Except: 2024/5/3)
Monday and Friday from 9:45 to 11:15. (Except: 2024/5/3)
From 2024/5/27 to 2024/6/17:
Each Monday from 8:00 to 9:30.
Each Monday from 9:45 to 11:15.