balkian/sitc
1
0
Fork 0
mirror of https://github.com/gsi-upm/sitc synced 2026-02-08 23:58:17 +00:00
Code Issues Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Carlos A. Iglesias
2026-02-04 18:49:16 +01:00
committed by GitHub
parent def1c06604
commit feb60748f4
1 changed files with 9 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
README.md
View File

@@ -1,7 +1,7 @@
# sitc # sitc
Exercises for Intelligent Systems Course at Universidad Politécnica de Madrid, Telecommunication Engineering School. This material is used in the subjects Exercises for Intelligent Systems Course at Universidad Politécnica de Madrid, Telecommunication Engineering School. This material is used in the subjects
- CDAW (Ciencia de datos y aprendizaje en automático en la web de datos) - Master Universitario de Ingeniería de Telecomunicación (MUIT) - CDAW (Ciencia de datos y aprendizaje en automático en la web de datos) - Máster Universitario de Ingeniería de Telecomunicación (MUIT)
- ABID (Analítica de Big Data) - Master Universitario en Ingeniera de Redes y Servicios Telemáticos) - ABID (Analítica de Big Data) - Máster Universitario en Ingeniería de Redes y Servicios Telemáticos)
For following this course: For following this course:
- Follow the instructions to install the environment: https://github.com/gsi-upm/sitc/blob/master/python/1_1_Notebooks.ipynb (Just install 'conda') - Follow the instructions to install the environment: https://github.com/gsi-upm/sitc/blob/master/python/1_1_Notebooks.ipynb (Just install 'conda')
@@ -10,12 +10,12 @@ For following this course:
Topics Topics
* Python: a quick introduction to Python * Python: a quick introduction to Python
* ML-1: introduction to machine learning with scikit-learn * ML-1: Introduction to machine learning with scikit-learn
* ML-2: introduction to machine learning with pandas and scikit-learn * ML-2: Introduction to machine learning with pandas and scikit-learn
* ML-21: preprocessing and visualizatoin * ML-21: Preprocessing and visualization
* ML-3: introduction to machine learning. Neural Computing * ML-3: Introduction to machine learning. Neural Computing
* ML-4: introduction to Evolutionary Computing * ML-4: Introduction to Evolutionary Computing
* ML-5: introduction to Reinforcement Learning * ML-5: Introduction to Reinforcement Learning
* NLP: introduction to NLP * NLP: Introduction to NLP
* LOD: Linked Open Data, exercises and example code * LOD: Linked Open Data, exercises and example code
* SNA: Social Network Analysis * SNA: Social Network Analysis