Introduction to Machine Learning

Introduction to Machine Learning#

Learning objectives#

Know what Machine Learning and Deep Learning are about.
Understand the main categories of ML systems.
Discover some of the many existing ML algorithms.

Environment setup#

import platform
from IPython.display import YouTubeVideo

print(f"Python version: {platform.python_version()}")

Python version: 3.11.1

Whats is Machine Learning?#

The first definition of Machine Learning#

“The field of study that gives computers the ability to learn without being explicitly programmed.” (Arthur Samuel, 1959).

Machine Learning in a nutshell#

Set 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 predictions.

As more data is fed into the system, results get better: performance improves with experience.

a.k.a. Statistical Learning.

A new paradigm…#

Programming paradigm

Training paradigm

… Or merely a bag of tricks?#

The Machine Learning landscape#

AI, Machine Learning and Deep Learning#

AI/ML/DL Venn diagram

Typology of ML systems#

ML systems are traditionally classified in three categories, according to the amount and type of human supervision during training. Hybrid approaches 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

Regression#

The system predicts continuous values. Examples: temperature forecasting, asset price prediction…

Regression example

Classification#

The system predicts discrete values: input is categorized.

Classification example

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.

Clustering#

Data is partitioned into groups.

ML clustering example

Anomaly Detection#

The system is able to detect abnomal samples (outliers).

ML anomaly detection example

Game AI#

How do machines learn, actually?#

Algorithm #1: K-Nearest Neighbors#

Prediction is based on the k nearest neighbors of a data sample.

Algorithm #2: Decision Trees#

Build a tree-like structure based on a series of discovered questions on the data.

Decision Tree for Iris dataset

Algorithm #3: Neural Networks#

Layers of loosely neuron-inpired computation units that can approximate any continuous function.

Neuron output

Dog or Cat?

The advent of Deep Learning#

The Deep Learning tsunami#

DL is a subfield of Machine Learning consisting of multilayered neural networks trained on vast amounts of data.

Since 2010, DL-based approaches outperformed previous state-of-the-art techniques in many fields (language translation, image and scene recognition, and much more).

Reasons for success#

Explosion of available data.
Huge progress in computing power.
Refinement of many existing algorithms.
Availability of sophisticated tools for building ML-powered systems.

TF, Keras and PyTorch logos

Big data universe

Computer power sheet