The course will provide both theoretical lessons and laboratory experience (especially a hands-on lab with real PLCs). Matlab and python code will be given to apply the theoretical concepts.

### PART I: Data science

Introduction to data science

The business perspective and the CRISP-DM process

Supervised vs. Unsupervised problems

Linear regression

Feasibility of learning

Bias-Variance tradeoff

Logistic regression

Overfitting and regularization

Validation and cross-validation

Performance metrics

Decision trees

Neural networks

Machine vision: classic approaches

Convolutional Neural Networks & deep learning

Object detection

Unsupervised learning

k-means clustering

Principal Component Analysis (PCA)

Introduction to fault diagnosis

Model-based FD: parity spaceobserver approach

Signal-based FD: bearing inner race pitting with vibration data

Data-driven FD: Statistical Process Monitoring with T^2 e Q statistics

### PART II: Automation

Introduction to industrial automation

Introduction to PLC

Ladder language

Structured text language

Automatic PLC code generation

Laboratory experience