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    Why this course?

    Mathematical optimisation is one of the cornerstones of fields such as Machine Learning, Artificial Intelligence, and Operations Research. Most decision support methods have, at some level, a mathematical optimisation method at its core, and it is precisely these methods that we will learn in this course.

    Linear Optimization is a powerful framework in which one seeks to represent systems using linear objective functions and constraints. Using the analogy that variables represent decisions or parameters to be defined, constraints represent rules that a valid configuration or a plan of action for the system, and the function is a measure of performance, one can use that framework to support decision-making in a wide range of applications, from planning industrial chemical plants to training models that learn from data. 

    In this course, the students will learn the basic linear optimisation theory as well as advanced algorithms available and how they can be applied to solve challenging real-world inspired optimisation problems. Throughout the course, we will also look into practical and research applications of linear optimisation. 

    Practical matters

    Lecturer: Fabricio Oliveira 
    Teaching Assistants: Olli Herrala (head), Helmi Hankimaa

    Teaching method

    • Lectures (2h per week) - Thursdays 14.15h-16.00h 
    • Exercise sessions (2h per week)
      • Tuesday 12.15h-14.00h (H1)
      • Monday 10.15h-12.00h (H3)
      • No exercise sessions in the first week of the course! Instead, you should take a look at the Intro to Julia section of the course and make sure you can run Julia code when the exercises start. 

    Lecture notes and forum for content questions:
    https://github.com/gamma-opt/optimisation-notes

    Artificial intelligence tools

    • Aalto level guidelines apply, specifically "AI-generated text cannot be presented as is as the student's own written response. The student is obligated to follow academic writing practices. Upon the teacher's request, the student is obligated to describe how, what and/or why AI-based technology has been used to do the learning task."
    • You may use artificial intelligence -based technology to support your learning. If artificial intelligence has been used in solving exercise problems, how and in which parts of the solution artificial intelligence has been used must be stated in the work
    • These instructions can change before (or during) the course as necessary


    "Administratrivia"


    If you have feedback at any point during the course, we have set up a virtual feedback box in Presemo (link). The feedback you submit there is anonymous and invisible to everyone except the lecturer and the head TA.
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