MACHINE LEARNING

This course provides an introduction to the principles, techniques, and applications of machine learning and artificial intelligence. The goal is to develop in students the critical skills needed to wisely choose and implement a data analysis solution based on machine learning techniques.

1. Knowledge and understanding
1.1. acquire the main models for automatic knowledge representation;
1.2. acquire the main models of supervised learning; and
1.3. acquire the main models of unsupervised learning

2. Ability to apply knowledge and understanding
2.1. know how to apply the models studied to real problems;
2.2. know how to critically evaluate the performance and behavior of a model applied to a concrete problem;

3. Judgment skills
3.1. know how to understand which features of various machine learning models best fit a given problem;
3.2. know how to critically evaluate the theoretical characteristics of proposed models;

4. Communication skills
4.1. Know how to communicate the results of an experiment using appropriate terminology;

5. Learning skills
5.1. Know how to critically consult reference texts and their bibliography.

Computational thinking, calculus, linear algebra, statistics, python programming

1. Introduction
1.1 What is learning?
1.2 What is and why machine learning?
1.3 Types of machine learning
1.4 ML in Environmental Science
1.5 ML Pipeline

2. Python and Colab - Recap

3. Data Preprocessing
3.1 Feature normalization/scaling
3.2 Data Imputation
3.3 Feature Selection/Reduction
3.4 Data visualization
3.5 Outlier removal

4. Supervised Learning
4.1 Training a model: dataset splits
4.2 Overfitting/Underfitting problem
4.3 k-NN, SVM, Decision Tree/Random Forest

5. Unsupervised Learning
5.1 k-Means
5.2 Hierarchical Clustering
5.3 DBSCAN
5.4 Spectral Clustering

6. Semi-Supervised Learning

7. AI/Deep Learning
7.1 Optimization, backpropagation, losses
7.2 Multi-Layer Perceptron
7.3 AutoEncoders
7.4 Convolutional Neural Networks
7.5 RNN/LSTM
7.6 Transformers

9. Environmental Science Applications

All study materials will be provided through Moodle.

The exam is divided into two parts:
- an oral exam on the entire course syllabus (70%)
- the development of a simple individual data science project in python (30%)

oral

A. Scores in the 18-22 range will be awarded in the presence of:
- Sufficient knowledge and ability to structure the project;
- Limited ability to justify implementation choices;
- Sufficient communication skills, especially in relation to the use of course-specific language.

B. Scores in the 23-26 range will be awarded in the presence of:
- Fair knowledge and ability to structure the project;
- Fair ability to collect and/or interpret data, proposing effective implementation solutions;
- Fair communication skills, especially in relation to the use of course-specific language.

C. Scores in the 27-30 range will be awarded in the presence of:
- Good or excellent knowledge and ability to structure the project;
- Good or excellent ability to collect and/or interpret data, proposing innovative implementation solutions;
- Fully appropriate communication skills, especially in relation to the use of course-specific language.

D. Lode will be awarded in the presence of excellent knowledge and applied understanding of the program, judgment skills, and communication abilities.

Powerpoint presentations and chalk talk.