Algorithm design

  • Teaching

    Details

    Faculty Faculty of Science and Medicine
    Domain Computer Science
    Code UE-SIN.08622
    Languages English
    Type of lesson Lecture
    Level Master
    Semester SS-2027

    Schedules and rooms

    Summary schedule Thursday 09:15 - 12:00, Hebdomadaire, PER 17, Room 001 (Spring semester)
    Struct. of the schedule 3h par semaine durant 14 semaines
    Contact's hours
    		42

    Teaching

    Responsibles
    • Ries Bernard
    Teachers
    • Dallard Clément
    Description

    The course explores algorithmic paradigms and methods that enable the development of efficient polynomial-time algorithms. Key paradigms discussed include greedy algorithms, divide and conquer, dynamic programming, and local search. These paradigms are used to develop efficient exact and approximation algorithms for a variety of problems motivated by real-life applications. Additionally, the course covers basic computational complexity concepts that establish theoretical limits on what can be solved efficiently.
    Training objectives

    By the end of this course, students should be able to:
    - Design and implement efficient algorithms using key paradigms such as greedy algorithms, divide and conquer, dynamic programming, and local search.
    - Develop both exact and approximation algorithms to solve real-world problems efficiently.
    - Analyze the performance of algorithms and understand the trade-offs between optimality and computational feasibility.
    - Apply basic concepts of computational complexity to determine the theoretical limits of efficient algorithms.
    Comments

    MSc-CS BENEFRI - (Code Ue: 43124 / Tracks: T4). The exact date and time of this course as well as the complete course list can be found at http://mcs.unibnf.ch/.

    Course and exam registration on ACADEMIA (not myunifr.ch). Please follow the instructions on https://mcs.unibnf.ch/organization/
    Softskills No
    Available online No
    Off field No
    BeNeFri Yes
    Mobility Yes
    UniPop No
  • Dates and rooms
    Date Hour Type of lesson Place
    25.02.2027 09:15 - 12:00 Cours PER 17, Room 001
    04.03.2027 09:15 - 12:00 Cours PER 17, Room 001
    11.03.2027 09:15 - 12:00 Cours PER 17, Room 001
    18.03.2027 09:15 - 12:00 Cours PER 17, Room 001
    25.03.2027 09:15 - 12:00 Cours PER 17, Room 001
    08.04.2027 09:15 - 12:00 Cours PER 17, Room 001
    15.04.2027 09:15 - 12:00 Cours PER 17, Room 001
    22.04.2027 09:15 - 12:00 Cours PER 17, Room 001
    29.04.2027 09:15 - 12:00 Cours PER 17, Room 001
    13.05.2027 09:15 - 12:00 Cours PER 17, Room 001
    20.05.2027 09:15 - 12:00 Cours PER 17, Room 001
    03.06.2027 09:15 - 12:00 Cours PER 17, Room 001
  • Assessments methods

    Exam

    Assessments methods By rating
  • Assignment
    Valid for the following curricula:
    Additional Courses in Sciences
    Version: ens_compl_sciences
    Paquet indépendant des branches > Specialized courses in Computer Science (Master level)
    Additional programme requirements for PhD studies [PRE-DOC]
    Version: 2020_1/v_01
    Additional programme requirements for PhD studies (Faculty of Science and Medicine) > Specialized courses in Computer Science (Master level)
    Computer Science [POST-DOC]
    Version: 2015_1/V_01
    Continuing education > Specialized courses in Computer Science (Master level)
    MSc in Computer science (BeNeFri)
    Version: 2023_1/V_01
    MSc in Computer science (BeNeFri), lectures, seminars and Master thesis > T4 : Theory and Logic
    Ma - Business Communication : Business Informatics - 90 ECTS
    Version: 2026-SA_V01
    Business Informatics > Business Informatics Courses > Module Informatik > Theory and Logic
    Business Informatics > Business Informatics Courses > Module Wirtschaftsinformatik > DADS: Data Analytics & Decision Support
    Ma - Business Informatics - 90 ECTS
    Version: 2020-SA_V01
    Classes - min. 45 ECTS > Module IT and IT Management - min 8 ECTS > Theory and Logic
    Classes - min. 45 ECTS > Module IT and IT Management - min 8 ECTS > DADS: Data Analytics & Decision Support
    Classes - min. 45 ECTS > Modules IT Management - min. 22 ECTS > DADS: Data Analytics & Decision Support
    MiMa - Business Informatics - 30 ECTS
    Version: 2020-SA_V01
    Courses > Modules in Business Informatics > DADS: Data Analytics & Decision Support
    Courses > Modules in Computer Science > Theory and Logic
    MiMa - Data Analytics - 30 ECTS
    Version: 2020-SA_V01
    Maximum of 9 ECTS credits can be chosen from courses in module T6 (Data Science) of the MSc in Computer Science and/or modules DADS and TMD of the MSc in Business Informatics. > DADS: Data Analytics & Decision Support