How to explain Machine Learning to my grandma.

Labels

Attribute

Samples

labeled examples: {features, label}: (x, y)
unlabeled examples: {features, ?}: (x, ?)

Models

Regression versus classification

First steps with TensorFlow: Toolkit

import tensorflow as tf # Configure un clasificador lineal. 
clasificador = tf . estimador . LinearClassifier ( feature_columns ) # Entrena el modelo en algunos datos de ejemplo. clasificador . train ( input_fn = train_input_fn , steps = 2000 ) # Úselo para predecir. predicciones = clasificador . predecir ( input_fn = predict_input_fn )

Training and test sets: data striping

Representation: Feature Engineering

Assigning numerical values

Representation: Data Cleaning

Handling outlier

Binning

[ 0 , 0 , 0 , 0 , 0 , 1 , 0 , 0 , 0 , 0 , 0 ]

Know Your Data

Classification: True Positive vs.Falso and vs.Negativo

Introduction to Neural Networks: Anatomy

Summary

Training of neural networks:

Neural networks multiple classes: One against all

Static vs. Dynamic Inference

Summary

Fairness: Types of Bias

Reporting Bias

Automation Bias

Selection Bias

Group Attribution Bias

Implicit Bias

Data Skew

ML Systems in the Real World: Cancer Prediction

ML Systems in the Real World: Literature

ML Systems in the Real World: Guidelines

Summary

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Carlos Alvarez

Carlos Alvarez

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