MS-C1650 - Numerical Analysis, Lecture, 25.4.2023-2.6.2023

Topic outline

• Select topic Spring 2023 Edition

  Spring 2023 Edition
  Exercise sessions start on week 2 of the course (2.-5.5.).

  There is no lecture on Thursday, 18.5. (public holiday). There is an extra lecture on Monday, 15.5.
  

  Midterm feedback: https://presemo.aalto.fi/numerics2023/

  This course is a gentle introduction to numerical analysis and is suitable for all engineering and mathematically oriented business school students. This is a mathematics course, that is, all computation has to be purposeful with clear understanding of errors and convergence properties of the methods. There will be five exercise sets and MyCourses quizzes. Almost every exercise set is structured as a Russian doll, where every subproblem develops on the previous one starting from theory and ending in implementation (MATLAB). Theory problems are submitted online, they are graded by our TAs, but scripts are subject to peer review. Simple model problems are graded with MyCourses provided quiz tools.
  

  Course details

  The course consists of 12 lectures, and 5 exercise classes. There are 5 rounds of homework assignment including peer grading assignments.
  

  Lecturer: Jonas Tölle, jonas.tolle@aalto.fi
  

  Head Assistant: Vili Kohonen, vili.kohonen@aalto.fi

  Assistant: Joona Lindell, joona.lindell@aalto.fi
  

  Teaching: 12 Lectures (contact teaching weeks 1–4, online teaching weeks 5–6), 5 exercise classes (contact-teaching)

  Lectures:  Tuesday, 14:15 – 16:00; Thursday, 14:15 – 16:00; one extra lecture on Monday, 15.5., 8:15-10:00

  Location: Lecture Hall U1, Otakaari 1, Espoo. Zoom (weeks 5–6 of the course).
  

  3 Exercises groups: Tuesday, 12:15–14:00, Wednesday, 12:15 – 14:00; Friday, 12:15 – 14:00, see Assignments section
  

  Assessment methods: Quizzes (10%), Homework (40%), MATLAB + peer grading (25%), Learning diary (25%).
  

  There is no exam in this course.
  

  Grading scale: 0–5

  Completing the course gives 5 ECTS credits.
  

  Study materials:

  Lecture notes by Harri Hakula are in handwritten and partially in typeset form and cover the main parts of the course.
  

  The lectures are inspired by Greenbaum & Chartier, which is the recommended text book. Ridgway Scott's book will be available as a PDF file.
  

  Literature:
  

  1. Anne Greenbaum and Tim P. Chartier. Numerical Methods: Design, Analysis, and Computer Implementation of Algorithms, Princeton University Press, 2012.
  

  2. L. Ridgway Scott. Numerical Analysis, Princeton University Press, 2011.

  3. Qingkai Kong, Timmy Siauw, and Alexandre Bayen. Python Programming and Numerical Methods. A Guide for Engineers and Scientists. Academic Press, 2020.
  

  4. Ernst Hairer, Gerhard Wanner, Syvert P. Nørsett. Solving Ordinary Differential Equations I: Nonstiff Problems. Springer, 2nd ed., 1993.

  Language of instruction: English

  Prerequisites: Differential and integral calculus 1–3.
  

  • Select activity Grading

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    Grading Page

    This is the preliminary grading (may be subject to change):

    Normalization of
    

    Quizzes (10%), Homework (40%), MATLAB + peer grading (25%), Learning diary (25%).

    Assuming max 100pts: 40pts are necessary to pass the course with grade 1.

    Scaling:

    Combined points	Grade
    40	1
    52	2
    64	3
    76	4
    88	5

    
    

    Last updated: June 22, 2023

  • Select activity Lecture Schedule

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    Lecture Schedule Page

    Weekly schedule as planned.

    Note that there is no lecture on 18.5. due to the Ascension Day public holiday. There is an extra lecture on Monday, 15.5.
    

    The lectures of the last 2 weeks are online in Zoom.

    Content may be subject to change. Last updated: May 31, 2023
    

  • Select activity Zoom for the last two weeks of the course

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    Zoom for the last two weeks of the course URL

    The lectures of the 2 weeks on Tue, 23.5., Thu, 25.5., Tue, 30.5., and Thu, 1.6. are in Zoom from 14:15–16:00.
    

  • Select activity Learning Diary (Instructions)

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    Learning Diary (Instructions) File PDF

    Here is a nice, concise explanation on what a learning diary is. This is shamelessly copied from a copy of the original in University of Helsinki. Use your own voice, add figures, draw, scribble, go wild. This could become a useful reference for you at the later stages of your studies, who knows.

    Some guidelines:

    • It is always good to structure your writing along the topics of the course from each week.
    • Reflection of one's own learning is appreciated.
    • It is good to have a literature / references list (with literature, course material, links to web resources, links to videos, indication of picture sources)
    • Take into account the page limits
    • Please use readable fonts only
    • It is a plus to add or discuss external resources
    • Clean presentation
    • Connection to other courses or real world problems

    
    

  • Select activity Frequently Asked Questions

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    Frequently Asked Questions Page

    A collection of questions by various students (and staff).

  • Select activity Announcements

    

    Announcements Forum
  • Select activity Discussion Forum

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    Discussion Forum

    Forum for discussing course content, lecture, homework and MATLAB exercises material.

    We will monitor the forum and offer help to questions when possible.
    

    Posts violating Aalto University's code of conduct will be removed.

    Posting of solutions to homework or code for the Matlab exercises is prohibited!