98743 - MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Learning outcomes

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.

Course 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

Readings/Bibliography

• 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.

Teaching methods

Frontal lessons

Assessment methods

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.

Teaching tools

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

Office hours

See the website of Matteo Amabili

See the website of Antonio Petruccelli