• Blended Learning & Flipped Classroom

Soft Computing includes technologies to model complex systems dealing explicitly with uncertainty, and offers powerful modeling tools for engineers and in general people needing to model complex phenomena. Among the application areas, we mention: (big) data analysis, classification, automatic control, robotics, modeling of artificial and natural phenomena, modeling of behaviors (e.g., of users and devices), decision support. The course will introduce rigorously the theoretical fundamentals of different modeling approaches, will put in evidence the application possibilities, by comparing different models, examples and application cases, will introduce design techniques for systems based on these technologies.

Goals for this course are listed in the following.

- Presentation of basic knowledge about some technologies within the Soft Computing area, namely: Fuzzy Systems, Genetic Algorithms , and Probabilistic Graphical Models (e.g., Bayesian Networks and Hidden Markov Models).

- Presentation of tools to implement the mentioned technologies.

- Analysis of paradigmatic case studies to understand the applicability issues of the mentioned technologies.

- Development of the ability to analyze a problem, to select the appropriate technology for a problem, to design data, architectures, and processes for the mentioned technologies

- Development of the ability to learn autonomously both declarative and procedural knowledge (thanks to innovative teaching methods, such as flipped class, and blended learning)

Acquisition of basic knowledge about some technologies within the Soft Computing area, namely: Fuzzy Systems, Genetic Algorithms, Bayesian Networks, Hidden Markov Models, (DD1)

Acquisition of the ability to analyze a problem, to select the appropriate technology for a problem, to design data, architectures and processes for the mentioned technologies (DD2, DD3)

Acquisition of basic operational abilities to implement the mentioned technologies. (DD2)

Acquisition of the ability to present both the knowledge, the process, and the proposed solutions, as well as to analyze results and data (DD4)

Acquisition of the ability to learn autonomously both declarative and procedural knowledge.

• What is Soft Computing: fuzzy systems, neural networks, stochastic algorithms and models
• Fuzzy models: fuzzy sets, fuzzy logic, fuzzy rules, motivations for fuzzy modeling, tools for fuzzy systems, design of fuzzy systems, applications.
• Bayesian networks: basics, design, learning, evaluation, applications
• Genetic Algorithms: basics, evolutionary computation, fitness function, design of applications, tools for genetic algoritms. 
• Applications: motivations, choices, models, case studies.

 No specific background is required.

The evaluation consists in a written exam where both theoretical competence and modeling skills will be tested. Attendance to lessons is important mainly to develop these last, which cannot be acquired from books, but only from experience.

Students are strongly advised to consider ALL the resources, included books and not only the slides.