DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE - 1

This course covers part of the "quantitative" aspects of the master program, and aims to provide the student with knowledge and skills on predictive data mining methods.
The goal of this course is to enable students the understand and exploit predictive data science techniques including both supervised (classification and regression) and un-supervised methods (clustering). The course includes the exploitation of data mining software tools through the python programming language.

The course discusses fundamental techniques for predictive and descriptive data science.

Students will achieve the following learning outcomes:

Knowledge and understanding: i) understanding principles of non-supervised learning; ii) understanding principles of supervised learning; iii) understanding principle of data pre-processing and feature engineering.

Applying knowledge and understanding: i) being able to apply supervised and unsupervised analysis techniques; ii) being able to use data analysis software tools (e.g., scikit-learn).

Making judgements: i) being able to choose the most appropriate method to a given problem and to evaluate its performance.

Communication: i) reporting comprehensive comparative analysis among different data analysis methods

Students should have achieved the learning outcomes of courses "Computer Programming And Data Management"
(even without passing the corresponding exams).

- Introduction to Data Science
- Feature engineering: text, numerical and categorical data; importance of similarity functions.
- Unsupervised Learning: clustering algorithms, k-means, hierarchical, db-scan; evaluation.
- Collaborative filtering: content-based and item-based recommendation algorithms.
- Supervised Learning: regression and classification algorithms; logistic classifier, SVM; decision trees; evaluation.
- Model tuning and Selection: bias and variance, overfitting, underfitting;
- Ensemble methods: Bagging, Boosting, Random Forest.

- Lecture notes. Selected readings provided during the course.
- Python Data Science Handbook. Jake VanderPlas. O'Reilly. 2016.
- Python Machine Learning - Third Edition. Sebastian Raschka, Vahid Mirjalili. Packt. 2019.

Learning outcomes are verified by a written exam and a project.

The written exam consists in questions and short exercise regarding the theory of the subjects discussed during the course. The written exam evaluates the theoretical knowledge gained by the student.

The project requires to conduct a comparative analysis of different tools applied to a specific dataset or problem.
The student must chose and motivate the most appropriate solution and deliver a report, to be discussed with the teacher. The project work evaluates the ability of the student in applying the theoretical knowledge to a real-world case study.

Lessons include both theoretical and hands-on sessions.
Teaching material is delivered through the Moodle platform.
During the course, the python programming language is used together with the scikit-learn library. Students are encouraged to bring their own laptops.

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