98743 - MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Conoscenze e abilità da conseguire

At the end of the course students will have a good knowledge of machine learning and Artificial Intelligence. They will be able to apply these techniques to problem related to climate change and sustainability.

Contenuti

The first part of the course is dedicated to an introduction of the main concepts of machine learning. Here the contents:

1. Intoduction to ML
   • 1.1.What is ML: a shift from knowledge to data
   • 1.2.Kind of problems
     • 1.2.1.supervised versus unsupervised
     • 1.2.2.regression vs classification
   • 1.3.Data pipeline
   • 1.4.Python Basics
     
   
   
2. Data Preprocessing
   • 2.1.Data Normalizzation
     
   • 2.3.Categorical variables: ordinal and non-ordinal
   • 2.4.Outliers
   • 2.5.Feature Engineering
   • 2.6.Dimensionality reduction:PCA
   • 2.7.Examples in python: sklearn
3. Linear Regression
   • 3.1.Estimating the coefficients: Least Square Method & maximum likehood
   • 3.2.Performance metrics
   • 3.3.Interpreting the coefficients
   • 3.4.The problem of Collinearity
   • 3.5.Selecting the relevant variables: Lasso/Ridge regression
   • 3.6.Kernel Regression
   • 3.7.Pyhton Hand-on
   
   
4. Logistic Regression
   • 4.1.Problem Definition
   • 4.2.Estimating the coefficients: gradient descent
   • 4.3.Classification Metrics:
     • 4.3.1.Precision
     • 4.3.2.Recall
     • 4.3.3.F-beta score
     • 4.3.4.Area Under tre ROC curve
   • 4.4.Interpreting the coefficients
   • 4.5.Generalized linear model: Poisson regression
   • 4.6.Multilabel case
   • 4.7.Python hands-on
   
   
5. Evaluate a Model
   • 5.1.Cross-validation & hyper parameter tuning
   • 5.2.Bias Variance trade-off
     
   • 5.3.Simple cross-validation
   • 5.4.N-fold cross-validation
   • 5.5.Python hands-on
   
   
6. Tree Based Method
   • 6.1.Simple Cart for regression and classification
   • 6.2.Ensample methods: Random Forest
   • 6.3.Boosting methods
   • 6.4.Python hands-on
   
   
7. Unsupervised learning
   • 7.1.Problems
   • 7.2.K-means
   • 7.3.Density-Based Model: DBSCAN
     
   • 7.5.Remove outliers using unsupervised methods
     
   • 7.6.Python Hands-on

The second part of the course (held by Prof. Petruccelli) is dedicated to the machine learning application to natural risk. Here is the contents:

1. Introduction to Natural Risks and Climate Change

• Usingtrees-based methods on natural disasters datasets

2. Geospatial Data Analysis

• Managing georeferenced data using appropriate libraries

3. Machine Learning in Weather and Climate

• Using pre-trained models for short-mid-range weather predictions

4. Extreme Events Analysis

• Performing analysis on extreme events using both classical and data-driven methods

5. AI for DisasterManagement

• Using computer vision and NLP algorithms to manage natural disasters use-cases

6. AI for Urban Vulnerability

• Using neural networks to segment and detect urban buildings

Testi/Bibliografia

• James, Gareth, et al. An introduction to statistical learning. Vol. 112. New York: springer, 2013.
• Hastie, Trevor, et al. The elements of statistical learning: data mining, inference, and prediction. Vol. 2. New York: springer, 2009.
• Rogers, Simon, and Mark Girolami. A first course in machine learning. Chapman and Hall/CRC, 2016.

Metodi didattici

Frontal lessons

Modalità di verifica e valutazione dell'apprendimento

The final exam will consists of a Machine learning project. During the exam, the student will have to present the developed project and discuss its main aspects as well as the underlying theory.

Strumenti a supporto della didattica

• Slides (power point/pdf)
• Selected literature
• Jupyter Notebook and Python Code

Orario di ricevimento

Consulta il sito web di Matteo Amabili

Consulta il sito web di Antonio Petruccelli