Introduction

to AI

Introduction to AI

What is AI ?

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.

These processes include :

• Learning
• Reasoning
• Self-correction

Introduction to AI

When we should use AI?

Artificial intelligence can be useful when :

• There is no expertise on the problem                    ex: robot flying on Mars

• We have the expertise, but we do not know how to explain it into an algorithm

                               ex: faces recognition

• Solutions to the problem change over time

                               ex: weather prediction

• Solutions must be customized

                               ex: center of interest

Introduction to AI

AI vs Machine Learning?

ML

DL

AI

Machine
Learning

Machine Learning

What is ML ?

Machine Learning is a field of AI based on :

• Neural Networks
• Logic / Probability

Kinds of machine learning

Supervised

Unsupervised

Reinforcement

Machine Learning

Supervised : Neural Network

Machine Learning

Supervised : Decision trees

Machine Learning

Supervised : Random-forests

Machine Learning

Supervised :

• Bayesian network

• Support vector machine

• K-NN

• Time Series Forcasting

Machine Learning

Internal cohesion

External insulation

• K-Means
• K-Medoids
• CLARA

Machine Learning

Reinforcement :

Desired

Behaviors

Undesired Behaviors

Rewards

Penalties

Deep

Learning

Deep Learning

" Modern problems require modern solutions "

Deep Learning

Auto encoder

Deep Learning

Convolutional Neural Network (CNN)

Deep Learning

Recurrent Neural Network (RNN)

How to!

(steps)

How to! (steps)

DATA

Preprocessing

Test &

Evaluation

Hyperparameters tuning

How to! (steps)

Data Preprocessing

• Data Collection

• Data Analytics

• Data Visualization

• Data Cleaning

• Data Standardization

• Features Extraction

• Data Decomposition

How to! (steps)

Hyperparameters tuning

• Optimizer
• Number of hidden layers
• Number of neurons in hidden layers
• Number of epochs
• Batch size
• Learning Rate
• Early stoping

How to! (steps)

Test & Evaluation

• Validation loss visualisation

• Test loss Visualisation

• HyperParameters tuning

• User Experiences