Master in Computer Science

Learning outcomes

Upon completion of this module, students will be able to:

• Develop a problem-solving strategy using graph data structures and algorithms
• Identify problems that can be modeled as graph problems
• Compare solutions (using complexity analysis)
• Write algorithms at a logical-mathematical abstraction level

Keywords: algorithm design, types of graphs, data structures, graph algorithms

Description

This first training-through-research project aims to put into practice some of the aspects of the research profession. We will begin by reading and analysing scientific articles in a restricted field that has already been defined. We will then go on to summarise some of the issues involved in this research. Finally, the experimental aspect will be addressed by reproducing and extending the experiments already carried out in these articles to measure the importance of rigour.

The evaluation will be made up of scores on a written summary, an oral summary and a work score given by the supervisor.

Learning outcomes

Upon completion of this module, students will be able to:

• Read and analyse state-of-the-art scientific articles provided by supervisors
• Understand the issues involved in this research and be able to summarise it in writing and orally
• Reproduce scientific experiments, paying particular attention to ethics, validation, etc.

Keywords: introduction to research, scientific writing

Description

This module aims to acquire the linguistic knowledge and skills necessary to communicate in French/English in simple everyday situations, on the one hand for the French language, and in the field of research, on the other hand for the English language.

Description

This course will be part of the EUR EIPHI common course pool.

Description

Artificial intelligence, frequently depicted as entities capable of thought, is a broad field encompassing the methods and models used to create intelligent agents. This course introduces a range of information modeling techniques and problem-solving approaches employed in AI.

Learning outcomes

Upon completion of this module, students will be able to:

• Understand the different paradigms of specification languages for program annotation
• Use specification languages to annotate and prove small programs
• Position the activities of program proving and testing
• Integrate this approach into program development (C and Java)

Keywords: deductive verification, Hoare logic, contract-based specification, proof tools

Description

This course provides students with the theoretical and methodological foundations necessary for conducting a research project. Topics covered include philosophy of science, research methods, ethics, and project management. Practical workshops complement these lectures by introducing students to literature review, scientific communication, and specialized software. Furthermore, laboratory visits and meetings with researchers allow students to explore various research environments and associated professions. Finally, a supervised research project offers a hands-on experience, enabling students to apply their acquired knowledge.

Learning outcomes

Upon completion of this module, students will be able to:

• Apply research design methods and tools
• Read and write scientific articles, research reports, and presentations
• Work independently and collaborate with other researchers
• Understand the ethical principles governing scientific research

Keywords: research methodology, research project

Description

The course addresses general issues of automata-based verification (modeling, verification, comparison relations, automata products), focusing on some examples of extensions (labeled transition systems, Büchi automata, probabilistic automata, ...), as well as on some examples of logics (temporal logic, modal logic).

The aim of this course is to delve deeper into the concept of automata, particularly in the context of system modeling and verification. Indeed, for reasons of expressiveness and algorithmic efficiency, many formal approaches to system modeling and analysis rely on enriched finite graphs, with states and transitions marked. This leads to various models of extended finite automata, for which it is necessary to maintain a balance between expressiveness and the algorithmic complexity of different problems. In parallel, compatible logics must be used to describe the desired properties.

Description

This teaching unit aims to provide students with the opportunity to conduct research within a team in the DISC department of FEMTO-ST.

Students select a research topic from the proposals offered by the teams and work on this topic under the supervision of a professor-researcher. This work spans an entire semester, involving one day of work per week.

Regular and progressive work is required. This includes writing intermediate documents, publishing code on a git repository, and writing a final report. Assessment is based on an oral presentation of the work, the review of the final report, and the supervisor's evaluation of the work accomplished.

Learning outcomes

Upon completion of this module, students will be able to:

• Analyze and communicate by identifying the processes of knowledge production, dissemination, and valorization
• Acquire a research theme and initiate some contributions

Description

The aim of this module is to study the relationship between the computer science and digital technologies sector and the various environmental and societal crises facing the world today. Through the study of various techniques from an ethical perspective, the module explores different tools, their impacts, and approaches to mitigate them. The module is situated within a current context in response to contemporary challenges, but also with a focus on foresight and anticipation to prepare students for emerging issues and the solutions that are beginning to emerge.

Learning outcomes

Upon completion of this module, students will be able to:

• Review of general impacts
• Energy measurement tools: watt, likwid, joularJX
• Life Cycle Analysis tools
• Focus on sub-domains and their specific challenges

Description

This module will introduce the machine learning paradigm, its foundations, and how an architecture should be designed to address a specific problem. We will explore how to structure data, how to build upon and customize existing architectures, how to analyze learning, and what tools to implement to analyze and optimize performance. Several practical exercises will be proposed in this regard.

Learning outcomes

Upon completion of this module, students will be able to:

• Describe the machine learning paradigm: presentation, foundations, key concepts
• Install a development environment
• Manipulate data and datasets
• Solving classification problems
• Analyse images
• Perform a research or bibliographic analysis on a specific problem

Description

This course will be part of the EUR EIPHI common course pool.

Description

This research project follows on from the research project 2 covered in S8, which is more closely supervised.

The aim here is to tackle an area of research in an exploratory manner: the subject will be designed in agreement with a supervisor who will provide an initial state of the art.

The student will have to supplement this with further reading, summarise it and come up with a more advanced problem.

This will be criticised and defended at intermediate points.

A survey article, a developed library and a demonstrator may be used as deliverables for the final assessment.

Learning outcomes

Upon completion of this module, students will be able to:

• Construct a detailed bibliography on an advanced research topic
• Synthesise the state of the art in a rigorous manner
• Develop publishable code

Description

Research internship at the FEMTO-ST/DISC laboratory (6 months).