Selected Topics in Computational and Applied Mathematics Ι

The main aim of this course is the study, in some depth, of subjects that come from any area of Computational and Applied Mathematics. These subjects can be chosen after discussion with the students. The basic characteristic of this course is the active participation of students via presentations. Some subjects of the course can be the following: Numerical Linear Algebra, Partial Differential Equations, Artificial Intelligence, Data Mining, Neural Networks etc.

• Trefethen L. N., Bau D., Numerical Linear Algebra, SIAM, 1997.
• Demmel J. W., Applied Numerical Linear Algebra., SIAM, 1997. ISBN 0-89871-389-7.
• Artificial Intelligence: A Modern Approach, 4th US ed., Stuart Russell and Peter Norvig  ISBN-13: 978-0134610993
• Z. Michalewicz, D. Fogel. How to Solve It: Modern Heuristics. 2nd edition, Springer, 2004.
• Data Mining and Machine Learning: Fundamental Concepts and Algorithms Second Edition Mohammed J. Zaki and Wagner Meira, Jr Cambridge University Press, March 2020 ISBN: 978-1108473989
• Introduction to Data Mining, Steinbach Tan, Kuma, ISBN-13: 978-9332571402
• Boyce W. E., Di Prima R. C, Elementary Differential Equations and Boundary Value Problems, 9th Edition,   John Wiley and Sons, Inc., 2009.
• Robinson J. C., An Introduction to Ordinary Differential Equations, Cambridge University Press, 2004.
• Hirsch M. W., Smale S.,  Devaney R., Differential Equations, Dynamical Systems and an Introduction to Chaos,  Elsevier 2004.
• Strauss W. A., Partial Differential Equations: An introduction, 2nd ed., John Wiley & Sons, Inc., 2008.
• Pinchover Y., Rubinstein J., An Introduction to Partial Differential Equations, Cambridge University Press, 2005.
• Haberman R., Applied Partial Differential Equations: with Fourier Series and Boundary Value Problems, 5th ed., Pearson, 2013.