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local
 
 
.gitignore
.gitignore
 
 
LAB 01.01 - PRACTICE SUBMISSION.ipynb
LAB 01.01 - PRACTICE SUBMISSION.ipynb
 
 
LAB 01.02 - METRICS.ipynb
LAB 01.02 - METRICS.ipynb
 
 
LAB 02.01 - PYTHON.ipynb
LAB 02.01 - PYTHON.ipynb
 
 
LAB 02.02 - NUMPY.ipynb
LAB 02.02 - NUMPY.ipynb
 
 
LAB 02.03 - PANDAS.ipynb
LAB 02.03 - PANDAS.ipynb
 
 
LAB 02.04 - CARD TRICK.ipynb
LAB 02.04 - CARD TRICK.ipynb
 
 
LAB 03.01 - MODEL GENERATION.ipynb
LAB 03.01 - MODEL GENERATION.ipynb
 
 
LAB 03.02 - TIMESERIES MODEL.ipynb
LAB 03.02 - TIMESERIES MODEL.ipynb
 
 
LAB 04.01 - CLEANING DATA.ipynb
LAB 04.01 - CLEANING DATA.ipynb
 
 
LAB 04.02 - BUILDING DATASETS.ipynb
LAB 04.02 - BUILDING DATASETS.ipynb
 
 
LAB 05.01 - MEASURING PREDICTIVITY IMPACT.ipynb
LAB 05.01 - MEASURING PREDICTIVITY IMPACT.ipynb
 
 
LAB 05.02 - MODEL EVALUATION.ipynb
LAB 05.02 - MODEL EVALUATION.ipynb
 
 
LAB 06.01 - CLUSTERING.ipynb
LAB 06.01 - CLUSTERING.ipynb
 
 
LAB 06.02 - NMF face search.ipynb
LAB 06.02 - NMF face search.ipynb
 
 
LAB 07.01 - OPTIMIZATION FOR ML.ipynb
LAB 07.01 - OPTIMIZATION FOR ML.ipynb
 
 
LAB 07.02 - NEURAL NETWORKS.ipynb
LAB 07.02 - NEURAL NETWORKS.ipynb
 
 
M00_20201udea.md
M00_20201udea.md
 
 
M01_videos.md
M01_videos.md
 
 
M02_videos.md
M02_videos.md
 
 
M03_videos.md
M03_videos.md
 
 
M04_videos.md
M04_videos.md
 
 
M05_videos.md
M05_videos.md
 
 
M06_videos.md
M06_videos.md
 
 
M07_videos.md
M07_videos.md
 
 
NOTES 01.01 - INTRODUCTION.pdf
NOTES 01.01 - INTRODUCTION.pdf
 
 
NOTES 01.02 - LOGISTICS.pdf
NOTES 01.02 - LOGISTICS.pdf
 
 
NOTES 02.01 - PYTHON BASIC STRUCTURES.ipynb
NOTES 02.01 - PYTHON BASIC STRUCTURES.ipynb
 
 
NOTES 02.02 - MORE PYTHON.ipynb
NOTES 02.02 - MORE PYTHON.ipynb
 
 
NOTES 02.03 - PYTHON NUMPY.ipynb
NOTES 02.03 - PYTHON NUMPY.ipynb
 
 
NOTES 02.04 - PANDAS.ipynb
NOTES 02.04 - PANDAS.ipynb
 
 
NOTES 03.01 - MODELS DERIVED FROM DATA.ipynb
NOTES 03.01 - MODELS DERIVED FROM DATA.ipynb
 
 
NOTES 03.02 - SUPERVISED ALGORITHMS.ipynb
NOTES 03.02 - SUPERVISED ALGORITHMS.ipynb
 
 
NOTES 03.03 - SVM AND FEATURE TRANSFORMATION.ipynb
NOTES 03.03 - SVM AND FEATURE TRANSFORMATION.ipynb
 
 
NOTES 04.01 - DATA EXPLORATION.ipynb
NOTES 04.01 - DATA EXPLORATION.ipynb
 
 
NOTES 04.02 - DATA CLEANING.ipynb
NOTES 04.02 - DATA CLEANING.ipynb
 
 
NOTES 04.03 - SIMPLE HYPOTHESIS TESTING.ipynb
NOTES 04.03 - SIMPLE HYPOTHESIS TESTING.ipynb
 
 
NOTES 04.04 - HYPOTHESIS TESTING - COMPARING SAMPLE MEANS.ipynb
NOTES 04.04 - HYPOTHESIS TESTING - COMPARING SAMPLE MEANS.ipynb
 
 
NOTES 05.01 - A TIME SERIES PREDICTIVE STRATEGY.ipynb
NOTES 05.01 - A TIME SERIES PREDICTIVE STRATEGY.ipynb
 
 
NOTES 05.02 - MODEL EVALUATION.ipynb
NOTES 05.02 - MODEL EVALUATION.ipynb
 
 
NOTES 05.03 - MODEL SELECTION.ipynb
NOTES 05.03 - MODEL SELECTION.ipynb
 
 
NOTES 05.04 - EXAMPLE SUBMISSION TO KAGGLE.ipynb
NOTES 05.04 - EXAMPLE SUBMISSION TO KAGGLE.ipynb
 
 
NOTES 06.01 - UNSUPERVISED LEARNING - CLUSTERING.ipynb
NOTES 06.01 - UNSUPERVISED LEARNING - CLUSTERING.ipynb
 
 
NOTES 06.02 - PCA.ipynb
NOTES 06.02 - PCA.ipynb
 
 
NOTES 06.03 - DIMENSIONALITY REDUCTION.ipynb
NOTES 06.03 - DIMENSIONALITY REDUCTION.ipynb
 
 
NOTES 07.01 - OPTIMIZATION.ipynb
NOTES 07.01 - OPTIMIZATION.ipynb
 
 
NOTES 07.02 - DESIGNING ML ALGORITHMS.ipynb
NOTES 07.02 - DESIGNING ML ALGORITHMS.ipynb
 
 
NOTES 07.03 - NEURAL NETWORKS.ipynb
NOTES 07.03 - NEURAL NETWORKS.ipynb
 
 
NOTES 07.04 - NEURAL NETWORKS WITH TENSORFLOW.ipynb
NOTES 07.04 - NEURAL NETWORKS WITH TENSORFLOW.ipynb
 
 
README.md
README.md
 
 
init.py
init.py
 
 

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Inteligencia Artificial para las Ciencias e Ingenierías

This repo contains the course's technical material. It is to be used only by the course developers.

For full interactive content access

https://rramosp.github.io/20201.xai4eng/

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Artificial Intelligence Course

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