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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.