MS-C1650 - Numerical Analysis, Lecture, 23.4.2024-31.5.2024
Kurssiasetusten perusteella kurssi on päättynyt 31.05.2024 Etsi kursseja: MS-C1650
Osion kuvaus
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The lectures on week 20 (14.5., 16.5.) are online in Zoom
https://aalto.zoom.us/j/61460384580
Exercise sessions start on week 2 of the course (29.4.-3.5.).
There is no lecture on Thursday, 9.5. (public holiday). There is an extra lecture on Wednesday, 8.5.
Midterm feedback:
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: Jaime Pardo, jaime.pardoherencia@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 Wednesday, 8.5., 14:15-16:00; no lecture on 9.5.
Location: Mostly Lecture Hall U1, Otakaari 1, Espoo, except for 28.5.: Y313, Otakaari 1, Espoo
3 Exercises groups: Tuesday, 12:15–14:00; Wednesday, 12:15 – 14:00 (except on 1.5.); 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. Tobin A. Driscoll and Richard J. Braun, Fundamentals of Numerical Computation, SIAM, 2017.
5. 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.
Intended learning outcomes:
After the course, the student will be able to...
- explain the fundamental concepts of numerical analysis, like condition number, stablilty, and convergence rate;
- construct the floating point numbers;
- discuss and employ basic numerical algorithms like Newton's method;
- use the Monte-Carlo method in basic problems in analysis and geometry;
- apply different methods of interpolation polynomials and numerical quadrature rules;
- understand the Euler scheme and linear multi-step methods for solving ordinary differential equations.
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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: April 10, 2024