Affective Computing
A.Y. 2021/2022
Learning objectives
The course describes and analyses theory and techniques for the design of affective artificial agents that are able to:
- perceive the user's affective signals and extract their most significant cues
- infer, form such cues in a given context, user's affective state
- provide the appropriate feedback to the user
To such end, the course concerns: a rigorous introduction to the neurobiological and psychological models of emotions; stochastic processes and statistical machine learning and inference for modelling the dynamics of affect
- perceive the user's affective signals and extract their most significant cues
- infer, form such cues in a given context, user's affective state
- provide the appropriate feedback to the user
To such end, the course concerns: a rigorous introduction to the neurobiological and psychological models of emotions; stochastic processes and statistical machine learning and inference for modelling the dynamics of affect
Expected learning outcomes
Upon completion of the course students will be able to:
1. Define the methodology and the most appropriate techniques for modelling affective agents dealing with uncertainty
2. Measure and analyse affective signals, either behavioural or physiological and extract affective cues
3. Design and implement simple affective agents to be exploited in different applications such as video surveillance, autonomous driving, robotics, entertainment.
These objectives are measured via a combination of three components: the project realisation, the project technical report and the oral discussion. The final grade is formed by assessing the software developed, the project report, and then using the oral discussion for fine tuning.
1. Define the methodology and the most appropriate techniques for modelling affective agents dealing with uncertainty
2. Measure and analyse affective signals, either behavioural or physiological and extract affective cues
3. Design and implement simple affective agents to be exploited in different applications such as video surveillance, autonomous driving, robotics, entertainment.
These objectives are measured via a combination of three components: the project realisation, the project technical report and the oral discussion. The final grade is formed by assessing the software developed, the project report, and then using the oral discussion for fine tuning.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Lectures will be given either in asynchronous or synchronous mode (or hybrid mode depending on the epidemiological scenario) via Zoom video conference system. In all modalities, lectures will be recorded and made available on the Ariel web site of the course
There will be no variations in the course program and examinations, except for the final oral discussion, which will take place on Zoom.
There will be no variations in the course program and examinations, except for the final oral discussion, which will take place on Zoom.
Course syllabus
The rationale behind affective modelling addresses the following problems:
- detection and analysis of emotional signalling and display
- perception of the context in which signalling takes place
- understanding/inference of the hidden affective state behind signalling
Such problems involve both the neurobiological/psychological level of affect and the statistical computational level for designing models that suitably address the above issues.
The course is articulated in two parts:
Part 1 Neurobiological and psychological models of emotions
- What is an emotion?
- Evolution and emotions
- Communication of emotions
- Emotions and the body
- Processing of autonomic signals
- Emotions and the brain
- Emotions, feelings and reason
Part 2 Probabilistic computational models of emotions
- Stochastic processes: probabilistic models
- Gaussian process modeling
- Continuous state Markov processes
- Approximate inference via Monte Carlo
- Approximate inference via Variational Bayes
- Deep generative neural networks and variational inference
- detection and analysis of emotional signalling and display
- perception of the context in which signalling takes place
- understanding/inference of the hidden affective state behind signalling
Such problems involve both the neurobiological/psychological level of affect and the statistical computational level for designing models that suitably address the above issues.
The course is articulated in two parts:
Part 1 Neurobiological and psychological models of emotions
- What is an emotion?
- Evolution and emotions
- Communication of emotions
- Emotions and the body
- Processing of autonomic signals
- Emotions and the brain
- Emotions, feelings and reason
Part 2 Probabilistic computational models of emotions
- Stochastic processes: probabilistic models
- Gaussian process modeling
- Continuous state Markov processes
- Approximate inference via Monte Carlo
- Approximate inference via Variational Bayes
- Deep generative neural networks and variational inference
Prerequisites for admission
Students are strongly encouraged to preliminarily attend the courses of Natural Interaction and Biomedical signal processing. Python programming capabilities are welcomed for project work.
Teaching methods
Lectures on general aspects of affective computing and, while developing the projects, hands-on activities within the PHUSE Lab
Teaching Resources
Lectures notes, live notes and slides, books, chapters, papers, videos and code are available on the course web site hosted by Ariel (https://gboccignoneac.ariel.ctu.unimi.it)
Assessment methods and Criteria
The examination concerns the development of a project and an oral discussion. The project is assigned based on ongoing affective computing research within the Perceptual computing and HUman SEnsing Laboratory (PHUSE Lab).
The project involves the realisation, and the experimental validation of a prototype addressing a specific case study. At the end of the project, the student should release a scientific report (illustrating the problem, results achieved, bibliography) and the code implementing the prototype.
The oral discussion aims at critically assessing project's motivation and results so far obtained, and the capability of the student to frame the latter within the theoretical topics presented in lectures.
The project involves the realisation, and the experimental validation of a prototype addressing a specific case study. At the end of the project, the student should release a scientific report (illustrating the problem, results achieved, bibliography) and the code implementing the prototype.
The oral discussion aims at critically assessing project's motivation and results so far obtained, and the capability of the student to frame the latter within the theoretical topics presented in lectures.
ING-INF/05 - INFORMATION PROCESSING SYSTEMS - University credits: 6
Lessons: 48 hours
Professor:
Boccignone Giuseppe
Professor(s)