01VIATD, 01VIAMY

A.A. 2023/24

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Digital Skills For Sustainable Societal Transitions - Torino

Master of science-level of the Bologna process in Geografia E Scienze Territoriali - Torino

Course structure

Teaching | Hours |
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Teachers

Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
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Teaching assistant

Context

SSD | CFU | Activities | Area context |
---|---|---|---|

ING-INF/05 | 6 | B - Caratterizzanti | Tecnologie dell'informatica |

2022/23

This course has a threefold objective:
1) to introduce to the student to the 'cultural' and technological issues related to AI and machine learning (ML)
2) to teach the use of machine learning techniques as a way to solve real problems
3) to gain practice implementing AI and ML algorithms and getting them to work for practical field of application

This course has a threefold objective:
1) to introduce to the student to the cultural and technological issues related to AI and machine learning (ML)
2) to teach the use of machine learning techniques as a way to solve real problems
3) to gain practice implementing AI and ML algorithms and getting them to work in the practical field of application

The student must acquire three fundamental types of knowledge:
1) the understanding of basics concepts (theory and models) of AI and ML
2) the ability to solve concrete problems applying ML algorithms using interactive frameworks and SW libraries
3) the ability to modify and adapt template ML algorithms for a new purpose
These skills will be applied to the solution of practical problems.

The student must acquire three fundamental types of knowledge:
1) the understanding of basics concepts (theory and models) of AI and ML
2) the ability to solve concrete problems applying ML algorithms using interactive frameworks and SW libraries
3) the ability to modify and adapt template ML algorithms for a new purpose
These skills will be applied to the solution of practical problems.

No special prerequirements are expected except some basics of programming language acquired in the course "Computer programming in Python".

No special pre-requirements are expected except some basics of programming language acquired in the course "Computer programming in Python".

THEORY [40 HRS]
- Hello AI and ML. We introduce the core idea of teaching a computer to learn models using data without being explicitly programmed.
- Background Concepts, Probability and Linear Algebra Review
- Representation of numerical and non-numerical data
- Linear Regression
- Neural Networks Representation and Learning
- Support Vector Machines
- Unsupervised Learning vs Supervised Learning
- Dimensionality Reduction
- Application Examples
LABS [21 HRS]
- Software Development Framework Tutorial
- Several assignments designed to help you understand how to implement the learning algorithms in practice.

THEORY [39 HRS - 4 CFU]
- We introduce the core idea of teaching a computer to learn models using data without being explicitly programmed.
- Background Concepts, Probability and Linear Algebra Review
- Representation of numerical and non-numerical data
- Linear Regression
- Neural Networks Representation and Learning
- Support Vector Machines
- Unsupervised Learning vs Supervised Learning
- Dimensionality Reduction
- Application Examples
LABS [21 HRS - 2 CFU]
- Software Development Framework Tutorial
- Several assignments are designed to help you understand how to implement the learning algorithms in practice.

The course includes about 21 hours of lab, in which the topics covered in the classes will be implemented using the development framework.

The course includes about 21 hours of lab, in which the topics covered in the classes will be implemented using the development framework.

Each week the student will attend 3 hours of lectures and 1.5 hours of exercises and laboratories.
The lessons will be divided into different types:
- Theory: illustration of theoretical topics (models, algorithms) with examples
- Problem solving: analysis of real problems and benchmarks, classroom discussion, design/adaptation of ML techniques to solve real applications
- Practice: illustration of the main ML modules available in the development framework and their demonstration using the PC

Each week the student will attend 3 hours of lectures and 1.5 hours of exercises and laboratories.
The lessons will be divided into different types:
- Theory: illustration of theoretical topics (models, algorithms) with examples
- Problem-solving: analysis of real problems and benchmarks, classroom discussion, design/adaptation of ML techniques to solve real applications
- Practice: illustration of the main ML modules available in the development framework and their demonstration using the PC

- Handouts of class material.
- On-line material with references provided during the course.

- Handouts of class material.
- On-line material with references provided during the course.
[1] Andriy Burkov 2019. The Hundred-Page Machine Learning Book.
[2] Stuart Russell and Peter Norvig 2020. Artificial Intelligence: A Modern Approach. Pearson Education (US)
[3] Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg.

...
The exam will consist of two parts:
1) A set of assignments addressing specific ML tasks to be developed during the course. For each assignment, instructors will provide a dataset and the students will have to develop analysis pipelines to solve the assigned problem (based on the topics presented during lectures and labs). Then, students should write a technical report detailing the employed methodology and a critical analysis of the obtained results. Both the designed pipeline and the report will be submitted by students to the instructor for evaluation.
2) A written test that aims at assessing the student's knowledge of the theoretical aspects of the course (through practical exercises and open-answer questions) and the ML design skills (through the designing of analysis pipelines that implement the solution of a practical problem for which datasets are provided).
The exam will assess the developed skills of the candidate to solve specific problems by applying the knowledge acquired during the course in the design of ML pipelines of data analysis.
The duration of the written test is 1.5 hours and it is a closed book test.
The maximum score for the written test is 30 cum laude.
During the discussion of the results of the written test, the instructor can request a supplementary oral test that can cover the whole course program and is meant to assess and elaborate the student's skills.

Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.

The exam will consist of two parts:
1) A set of assignments addressing specific ML tasks to be developed during the course. For each assignment, instructors will provide a dataset and the students will have to develop analysis pipelines to solve the assigned problem (based on the topics presented during lectures and labs). Then, students should write a technical report detailing the employed methodology and a critical analysis of the obtained results. Both the designed pipeline and the report will be submitted by students to the instructor for evaluation.
2) A written test that aims at assessing the student's knowledge of the theoretical aspects of the course (through practical exercises and open-answer questions) and the ML design skills (through the designing of analysis pipelines that implement the solution of a practical problem for which datasets are provided).
The exam will assess the developed skills of the candidate to solve specific problems by applying the knowledge acquired during the course in the design of ML pipelines of data analysis.
The duration of the written test is 1.5 hours and it is a closed book test.
The maximum score for the written test is 30 cum laude.
During the discussion of the results of the written test, the instructor can request a supplementary oral test that can cover the whole course program and is meant to assess and elaborate the student's skills.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.

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Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY

Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY