<jupyter_start><jupyter_text>Introduction to Machine Learning  The Machine Learning paradigm  The first definition> "The field of study that gives computers the ability to learn without being explicitly programmed" (<NAME>, 1959).  Machine Learning in a nutshellSet of techniques for giving machines the ability to to find **patterns** and extract **rules** from data, in order to:* Identify or classify elements* Detect tendencies* Make predictionsAs more data is fed into the system, results get better: performance improves with experience.a.k.a. **Statistical Learning**.  A new paradigm ![Programming paradigm](images/programming_paradigm.png) ![Training paradigm](images/training_paradigm.png) [![ML on XKCD](images/ml_xkcd.png)](https://xkcd.com/1838/)  The Machine Learning landscape ![AI/ML/DL Venn diagram](images/ai_ml_dl.png)  Typology of ML systemsML systems are traditionally classified in three categories, according to the amount and type of human supervision during training. [Hybrid approaches](https://hackernoon.com/self-supervised-learning-gets-us-closer-to-autonomous-learning-be77e6c86b5a) exist.- **Supervised Learning**: expected results (called *labels* or *tags*) are given to the system along with training data.- **Unsupervised Learning**: training data comes without the expected results. The system must discover some structure in the data by itself.- **Reinforcement Learning**: without being given an explicit goal, the system's decisions produce a **reward** it tries to maximize. ![ML category tree](images/ml_tree.png)  RegressionThe system predicts **continuous** values. Examples: temperature forecasting, asset price prediction...![Regression example](images/ml_regression.png)  ClassificationThe system predicts **discrete** values: input is **categorized**.![Classification example](images/ml_classification.png)  Classification types- **Binary**: only two possibles classes. Examples: cat/not a cat, spam/legit mail, benign/malignant tumor.- **Multiclass**: several mutually exclusive classes. Example: handwritten digit recognition.- **Multilabel**: several non-mutually exclusive classes. Example: face recognition.   ClusteringData is partitioned into groups.![ML clustering example](images/ml_clustering.png)  Anomaly DetectionThe system is able to detect abnomal samples (*outliers*).![ML anomaly detection example](images/ml_anomaly_detection.png)  Game AI<jupyter_code>from IPython.display import YouTubeVideo

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