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. 2016-2021

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.

written and oral

The exam will include a written test, which is mandatory, and a group project, which is optional and can allow to obtain up to three extra points.

Written exam (mandatory)

To be undertaken in presence at the end of the course (i.e. from the end of may).

This written exam will include three theoretical questions, about topics covered during the whole course, and a small exercise, asking to design from scratch a solution to a practical problem.

Each theoretical question will grant up to 4 points and the exercise will grant up to 20 points.

The students must submit the answers to the theoretical questions within 30 minutes form the start of the exam and the whole solution must be submitted within 90 minutes overall (i.e. if the student submits the theoretical questions in advance, he/she will have more time available for the exercise).

Group project (optional)

Students are encouraged to build groups from 1 to (max) 4 people. Each group must choose a groupname and send me a mail with (exactly) "[EM1405 Group Composition]" in the subject and the name and members of the groups in the text.

Each group will choose a data-driven problem of interest, will find the relevant data and will devise and implement all the tool needed to solve it. During the course several suggestions will be supplied, however the students are more that welcome to bring their very own ideas. Additionally, a few selected companies (especially during the second part of the course) will be invited to talk about relevant use cases and introduce challenges that can be addressed as a group project.

The group can submit the project any moment from the end of the course. There is not any time constraint for the presentation, however the final mark will be assigned when both the written exam and the project have been completed.

The submission must include:

A slide-based presentation of the activity (goals, methods, data used, approch, results)
Links to supporting materials (notebooks, data, etc.)

Should a student of the group decide to opt-out from the project, he/she can do anytime and get the grade of the written exam registered.

The group project will grant a maximum of 3 extra points to be added to the grading of the written exam.

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.