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Lesson 1.2 - Machine Learning

What is machine learning really?

Artificial Intelligence: A program that can sense, reason, act and adapt--> 1950s: creation of first “intelligent” algorithms and programs 

Machine Learning: Algorithms whose performance improve as they are exposed to more data over time --> 1980s: statistical models and algorithms that can learn from data

Deep Learning: Subset of machine learning in which multilayered neural networks learn from vast amounts of data --> 2010s: statistical models and algorithms inspired by neurones that can learn from data

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Machine Learning Branches

3 Main Branches:

- Supervised Learning - Unsupervised Learning - Reinforcement Learning

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Supervised Learning

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In supervised learning the training data consists of input/output pairs and we train a function to map the inputs to the outputs.

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Supervised Learning: Classification

Classification: Assign categorical labels from a fixed set of labels to data samples.

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Supervised Learning: Regression 

Regression: Find the relationship between one dependent variable and one or more input variables.

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Machine Learning Branches: Unsupervised Learning

In unsupervised learning there are no labels available, insights are gained without* prior knowledge.

* Usually some model parameters need to be set ahead of training.

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Unsupervised Learning: Anomaly/Outlier detection

Anomaly Detection: The task of finding samples in a dataset that raise suspicion.

Problem: Usually, what exactly you are looking for is unknown.

Solution: Use statistics and characteristics of dataset to find outliers.

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Deep Learning

Why now? In recent years two things became available:  

1. A lot of data

2. Necessary computational power