- Docente: Matteo Amabili
- Crediti formativi: 6
- SSD: SECS-S/06
- Lingua di insegnamento: Inglese
- Moduli: Matteo Amabili (Modulo 1) Antonio Petruccelli (Modulo 2)
- Modalità didattica: Convenzionale - Lezioni in presenza (Modulo 1) Convenzionale - Lezioni in presenza (Modulo 2)
- Campus: Bologna
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Corso:
Laurea Magistrale in
Greening Energy Market and Finance (cod. 5885)
Valido anche per Laurea Magistrale in Quantitative Finance (cod. 8854)
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Orario delle lezioni (Modulo 1)
dal 15/02/2024 al 29/02/2024
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Orario delle lezioni (Modulo 2)
dal 01/03/2024 al 15/03/2024
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:
- 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
- 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
- 2.1.Data Normalizzation
- 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
- 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
- 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
- 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
- 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 parte of the course (held by Prof. Petruccelli) is dedicated to the machine learning application to natural risk. Here is the contents:
- Introduction to Natural Risks
- Geospatial Data Analysis
- Machine Learning in Weather and Climate
- Extreme Events Analysis
- AI and Disaster Management
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