General information
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Teacher: Marco Chiarandini
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Teacher assistants: Casper Asbjørn Eriksen (DM579: H1, DM587: H1), Muhammad Rajabinasab (DM579: H2)
Schedule
Contents
Introductory Classes
Week | Date | Topics and Slides | Suggested reading |
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43 | Oct 23 | Python - Part 1: basics, data types, control flow | App A, B and ch 1-3 of [HJ1]; [DB] |
43 | Oct 25 | Python - Part 1b: std library, OO progr. | App A, B and ch 1-3 of [HJ1]; [DB] |
44 | Oct 31 | Python - Part 2: exceptions, file i/o, numpy | Ch 4,6 of [HJ1]; [NS] |
44 | Nov 2 | Python - Part 3: graphics, data viz, pandas | Ch 5,8 of [HJ1]; Sc 6.3-6.5 of [AR]; (Ch 9 of [HJ2]) |
45 | Nov 9 | Least Squares Data Fitting | [MC]; Sc. 6.3-6.5 of [AR] |
46 | Nov 16 | Graph Isomorphism and Molecules | [Wi, ch 2,3] (voluntary reading), script, Additional slides on Ullmann algorithm] |
47 | Nov 23 | From Random Polygon to Ellipse | Article (pp 1-5,17); Video (optional) |
48 | Nov 30 | Page Rank | Sc. 4.12, 9.2-9.3, 10.5, 10.17 of [AR]; [LM] |
49 | Dec 4 | Principal Component Analysis and Eigenfaces | Visual Intro; Article (Tutorial on PCA); Chapter 7 of [HJ] (Facial Recognition); PCA in Three Steps; Eigenfaces (OpenCV based); Open CV |
50 | Dec 11 | Linear Programming and Numerical Methods | LP, Affine, LU, Ch 21 [V]; Sc 10.2, 9.1, 9.2, 9.3 and 10.8[AR] |
Exercises and Assignments
Week | Sheet | Topic | Solutions | Assignments |
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43 | colab1; sheet1 | Python, Part 1 and 2 | sheet1 | asg0 |
44 | sheet2 | Python, Part 3 and 4 | sheet2 | asg1 |
45 | sheet3 | Least Squares | sheet3 | asg2 |
46 | sheet4 | Graph Theory | asg3 | |
47 | sheet5 | From Random Polygon to Ellipse | asg4 | |
48 | sheet6 | Page Rank | asg5 | |
49 | sheet7 | Eigenfaces / PCA | asg6 | |
50 | sheet8 | Linear Programming | sheet8 |
References
Recommended book:
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[HJ1] Python Essentials. Jeffrey Humpherys and Tyler J. Jarvis, managing editors
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[HJ2] Data Science Essentials. Jeffrey Humpherys and Tyler J. Jarvis, managing editors
Other References:
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[DB] David Beazley. Python Tutorial Video
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[HJ] Jeffrey Humpherys and Tyler J. Jarvis. Foundations of Applied Mathematics. Volume 1. Mathematical Analysis. 2017. Society for Industrial and Applied Mathematics.
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[AR] Howard Anton and Chris Rorres. Elementary Linear Algebra With Supplemental Applications. Edition International Student Version. 11th Edition. 2014. Wiley
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[PK] Philip N. Klein. Coding the Matrix: Linear Algebra through Applications to Computer Science. 1st Edition. Newtonian Press; 1 edition, September 3, 2013
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[AH] Martin Anthony and Michele Harvey, Linear Algebra, Concepts and Methods. 2012. Cambridge
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[Le] Steven J. Leon, Linear Algebra with Applications, Prentice Hall (2010).
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[FSV] Computing with Python: An introduction to Python for science and engineering Claus Führer, Jan Erik Solem, Olivier Verdier
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[SAA] Immersive linear algebra by J. Ström, K. Åström, and T. Akenine-Möller. 2017. for $x$ in $(-1, 1)$
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[MC] Lecture Notes on Linear Regression by Marco Chiarandini.
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[NS]
numpy
API reference and its submodule linalg; andscipy
API reference and its submodule linalg. (scipy
is a superset ofnumpy
.) -
[Wi] Introduction to Graph Theory, Robin J. Wilson, 5th edition, 2010.
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[BP] Brin, Sergey; Page, Lawrence. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems. Volume 30, Issues 1–7, April 1998, Pages 107-117
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[PBMW] Page, Lawrence; Brin, Sergey; Motwani, Rajeev and Winograd, Terry, The PageRank citation ranking: Bringing order to the Web. 1999
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[LM] Amy N. Langville; Carl D. Meyer. A Survey of Eigenvector Methods for Web Information Retrieval. SIAM Review, Society for Industrial and Applied Mathematics, Vol. 47, No. 1, pp. 135–161
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[V] Robert J. Vanderbei, Linear Programming: Foundations and Extensions, Fourth Edition, Springer. 2014.