Educational objectives General objectives.
The aim of the course is to provide an overview to specific research topics. The topics are presented by active researchers in order to present the student with research problems and relevant and recent application themes in Artificial Intelligence. To this end, the courses include both the presentation and discussion of scientific articles, and an advanced project work.
The learning objective of the course is to provide the knowledge needed to undertake research work in these fields using practical tools for experimental validation.
Specific objectives.
Knowledge and understanding:
The topics are covered by researchers active in the field and with the aim of introducing the student to research problems and recent and relevant applications in Artificial Intelligence and Robotics.
Applied knowledge and understanding:
The course provides the knowledge necessary to undertake research work in these fields using practical tools for experimental validation.
Critical and judgment skills:
The course proposes advanced methods to study, understand and apply results reported on scientific articles, and integrate these results to create innovative Artificial Intelligence applications. The student learns how to use results from the literature as a basis for new research.
Communication skills:
Group activities in the classroom and the need to make presentations to the class allow the student to develop the ability to communicate and share the knowledge acquired and to compare herself with others on the topics of the course.
Learning ability:
In addition to the classic learning skills provided by the theoretical study of the teaching material, the course develops methods stimulate the student to deepen his knowledge of some of the topics she presents to the course and to the work group. Furthemroe the course stimulates the student to effectively apply both the concepts and the techniques learned during the course.
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Educational objectives General Objectives
The "Network Infrastructures" course provides an in-depth overview of the main architectures, protocols, and technologies of modern network infrastructures, with a particular focus on broadband access networks, optical transport networks, and next-generation wireless solutions. Students will gain a detailed understanding of the fundamental technologies and protocols for configuring and managing telecommunications networks, covering both theoretical and practical aspects. The course includes hands-on exercises on network configurations using advanced simulation tools, developing essential operational skills in the telecommunications sector. Additionally, key network security solutions and Quality of Service (QoS) support mechanisms will be analyzed, preparing students to understand and address emerging challenges in network infrastructures.
Specific Objectives
Knowledge and understanding: Students will acquire a deep understanding of network architectures, access technologies (xDSL, PON, LTE, 5G), transport protocols (OTN, MPLS), and routing mechanisms.
Applying knowledge and understanding: Students will be able to configure, analyze, and troubleshoot IP networks using simulation tools such as Kathara.
Autonomy of judgment: Students will develop the ability to critically evaluate different network technologies and select optimal solutions based on security, performance, and scalability requirements.
Communication skills: Students will be able to present network technology concepts clearly in both technical and general contexts.
Learning skills: The course will provide methodological foundations to keep up with the continuous evolution of network technologies and independently explore new solutions and emerging standards in the telecommunications sector.
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Educational objectives General Objectives.
The Reinforcement Learning (RL) course aims to introduce students to fundamental and advanced techniques of RL, a significant area within artificial intelligence and machine learning. Students will gain skills to design and implement algorithms that enable systems to learn and improve autonomously through experience, optimizing their decisions in real-time.
Specific Objectives.
Students will explore key concepts of RL such as decision policies, Markov Decision Processes, Q-learning, and deep reinforcement learning. They will learn to:
Model complex problems using the RL approach.
Develop and implement algorithms like Q-learning and Deep Q-Networks (DQN).
Apply RL techniques in real-world scenarios like robotics, gaming, etc.
Knowledge and Understanding:
In-depth knowledge of basic and advanced RL algorithms.
Understanding of reward-based learning models and their practical applications.
Ability to interpret the results of RL algorithms and evaluate their effectiveness in various contexts.
Applying Knowledge and Understanding:
Use software frameworks like TensorFlow or PyTorch to implement and test RL algorithms.
Analyze current research case studies and projects to understand real-world RL applications.
Develop functional prototypes using RL to solve specific problems.
Autonomy of Judgment:
Students will develop the ability to critically assess RL algorithms, considering their applicability, efficiency, and potential biases. They will also be able to select the most appropriate algorithm for a given problem.
Communication Skills:
Students will learn to effectively communicate RL concepts, algorithm design decisions, and outcomes to both technical and non-technical audiences using a variety of communication media.
Next Study Abilities:
This course will prepare students to pursue advanced studies and research in RL, providing the necessary foundation to tackle open problems and innovate in the field. Students will be encouraged to actively contribute to the scientific community through publications, conferences, and collaborations.
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Educational objectives General Objectives:
Acquiring knowledge on the basic tools for probabilistic state estimation in robotics.
Being able to apply these tools to real study cases and to implement working solutions.
Evaluate the quality of a state estimator.
Specific Objectives:
Knowledge and Understanding:
- how to manipulate probability distributions, in particular Gaussians
- the basics of filtering (hisrogram filters, Gaussian filters, particle filters)
- the generic model for a stationary non-linear or linear
- Dense and Sparse formulation of minimization algorithms (Gauss-Newton, Levenberg Marquardt)
- The problem of Data Association, and typical tools to approach it (RANSAC, Heuristics)
- Typical study cases of estimation problems in robotics (Calibration, Localization, Mapping and SLAM)
Applying Knowledge and Understanding:
- Being able to model a problem and to adapt the tools to its solution.
- Develop a functioning estimator.
Making Judgements:
- Being able to analyze the pros and contra of different solutions to the same problem.
- Spot the tools applicable to solve all subtasks in the design of an estimator.
These abilities are supported by the Project to be developed as a part of the exam.
The course interleaves theory and practice. During the practicals the students are asked to
complete code snippets provided by the teacher and to run their programs on real study cases.
Communication Skills:
- Acquire a common language to describe estimators and a development methodology
that supports interaction between developers by defining a standard set of goals.
Learning Skills:
The student will possess the abilities and the skills to approach general estimation problems.
The examples in the domain of navigation provided during the course serve as study cases.
The indivudal topics learned (Gaussian Manipulation, Filtering Designs, Minimization)
are useful instruments to approach a far more general class of problems
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Educational objectives General objectives:
The course is intended as a broad overview to neural networks, as used today in a number of applicative fields. It provides a strong theoretical and practical understanding of how neural networks and modern deep networks are designed and implemented, highlighting the most common components, ideas, and current limitations.
Specific objectives:
From a theoretical point of view, we will review the general paradigm of building differentiable models that can be optimized end-to-end with gradient descent from data. We will then overview essential components to design architectures able to work on images (convolutive layers), sequences (recurrent layers), and sets (transformer layers). The last part of the course will then focus on a selection of important research topics, including graph neural networks, continual learning, and generative models.
Knowledge and understanding:
At the end of the course, the student will have a broad understanding of how deep networks work in practice, with the capability of implementing new components from scratch, re-using existing models, or designing new architectures for problems beyond the overview of the course.
Critical and judgment skills:
The student is expected to be able to analyze a new problem requiring machine learning, and design the appropriate neural network based solution to tackle it, understanding both its strengths and its drawbacks.
Communication skills:
The course will foster communication skills in terms of being able to describe (in both a technical and non-technical way) the mathematics underlying the models, as long as writing clear and understandable code for its implementation.
Learning ability:
Beyond the topics of the course, the student will be able to autonomously study new topics on the research frontier, and navigate the current scientific literature and software panorama.
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Educational objectives This course introduces the main ideas of automated planning and mechanism for
formal logic reasoning within the field of artificial intelligence. The aim of
the sources is to prepare the student so that they can use the existing systems
for automated planning and understand their inner workings, which is
fundamental to adapt them to cope with issues arising from specific problems.
Furthermore, the student will understand the theoretical bases of the uses of
formal logics in artificial intelligence.
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