Machine Learning
Máster en Sistemas Inteligentes en Energía y Transporte
2024-12-15
About these notes and the course
Course
- Syllabus year 24-25: SISTEMAS INTELIGENTES PARA EL PROCESADO DE DATOS Y AYUDA A LA DECISIÓN
- Campus Virtual 24-25
- The course is divided into two blocks: Machine Learning and Data Mining. All what follows is just about the Machine Learning part
Notes
- These notes are being continuously updated. Check you have the latest version
- These notes are intended as a script to the classes
- They do not definitely contain all the information given in the course and required in the exercises
- The code chunks are examples shown in class and the code is not particularly debugged
- Running the code without a careful read and thorough check is explicitly discouraged.
- Each class contains exercises that must be done during the class
Evaluation
- The assignments will be published and submitted through Campus Virtual
- There are three assignments, one at the end of each section
- The deadline for assignment is one week after the last class of the section
- Assigments will be submitted in RMarkdown format
- The assignments are a compilation and rethinking of class exercises, thus it is very important to do each exercise set during the corresponding class
- The code quality is irrelevant… but it must run without errors!! You are free to use the code included in these notes, as long as you read it and understand it
- All results must be explained and discussed
- The submitted exercises are your sole responsibility and you should stand by both code and results if asked
- Strictly answering the questions posed in the assignment leads to the minimum passing grade. It is necessary to look for additional information and ellaborate the results to raise the mark.
References
An introduction to statistical learning
James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert
https://www.statlearning.com/The Elements of Statistical Learning
Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome
https://web.stanford.edu/~hastie/ElemStatLearn/R for Data Science
Hadley Wickham and Garrett Grolemund
https://r4ds.had.co.nz/index.htmlArtificial Neural Networks. A Practical Course
Ivan Nunes da Silva, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, Silas Franco dos Reis Alves
https://link--springer--com.uma.debiblio.com/book/10.1007/978-3-319-43162-8Applied Machine Learning
Forsyth, David
https://courses.engr.illinois.edu/cs498aml/sp2019/