University of West Attica
Artificial Intelligence and Data Science
Course info:
Semester: 3
General Foundation
ECTS: 6
Hours per week: 3
Professor: T.B.D.
Teaching style: Face to face, distance learning, project-oriented education
Grading: Written report, public presentation
| Activity | Workload |
|---|---|
| Lectures | 36 |
| Non-guided study | 62 |
| Report writing | 52 |
| Course total | 150 |
Learning Results
After the successful completion of this course, students will understand the link between data sciences/artificial intelligence and optimization problems. Consequently, students will: 1) understand the need for efficient numerical methods, 2) become familiar with numerical methods of solving problems and optimizing solutions, familiar to them from their previous studies, 3) become familiar with programming in both MATLAB and R environments, 4) be able to program their own implementations of numerical methods.
Skills acquired
Research, analysis and synthesis of the data and information, using the appropriate equipment, Working into an interdisciplinary environment, Work autonomously, work in teams, Producing new research ideas, Promotion of free, creative and inductive thinking.
Introduction to Numerical Methods, Introduction to R, Numerical Methods in R, Introduction to MATLAB, Numerical Methods in MATLAB, Arrays/Vectors in MATLAB/R, Plots in MATLAB/R, Solving algebraic equations (Bisection method, Newton’s Method, etc), Solving systems of algebraic equations (QR, Singular, Eigen, LU, etc), Systems of nonlinear equations, Numerical Differentiation and Integration, Optimization, Analyzing data, Fitting models to data, Interpolation.
- James P. Howard II, “Computational Methods for Numerical Analysis with R (Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series)”, 1st edition, Chapman and Hall/CRC, 2017, ISBN: 978-1498723633
- Owen Jones, Robert Maillarder, Andrew Robinson, “Introduction to Scientific Programming and Simulation Using R”, 2nd edition, Chapman and Hall/CRC, 2014, ISBN: 978-1466569997
- Alfio Quarteroni, Fausto Saleri, Paola Gervasio, “Scientific Computing with MATLAB and Octave”, 4th edition, Springer, 2014, ISBN: 978-3642453663
- Eihab B. M. Bashier, “Practical Numerical and Scientific Computing with MATLAB® and Python”, 1st edition, CRC Press, 2020, ISBN: 9780367076696
- S. D. Conte, Carl De Boor, “Elementary Numerical Analysis: An Algorithmic Approach, updated with MATLAB”, 1st edition, SIAM-Society for Industrial and Applied Mathematics, 2017, ISBN: 978-1611975192
- Nabil Nassif, Dolly K. Fayyad, “Introduction to Numerical Analysis and Scientific Computing”, 1st edition, Chapman and Hall/CRC, 2013, ISBN: 978-1466589483
- Victor A. Bloomfield, “Using R for Numerical Analysis in Science and Engineering”, 1st edition, Routledge, 2014, ISBN: 978-1439884485
Additional suggested bibliography:
-
Cymra Haskell, “Introduction to Scientific Programming with MATLAB”, 2010, available at https://dornsife.usc.edu/assets/sites/372/docs/Software_Resources/Matlab/cover.pdf
-
Cesar Lopez, “MATLAB Programming for Numerical Analysis (Matlab Solutions)”, 1st edition, Apress, 2014, ISBN: 978-1484202968
- Learning Results - Skills acquired
-
Learning Results
After the successful completion of this course, students will understand the link between data sciences/artificial intelligence and optimization problems. Consequently, students will: 1) understand the need for efficient numerical methods, 2) become familiar with numerical methods of solving problems and optimizing solutions, familiar to them from their previous studies, 3) become familiar with programming in both MATLAB and R environments, 4) be able to program their own implementations of numerical methods.
Skills acquired
Research, analysis and synthesis of the data and information, using the appropriate equipment, Working into an interdisciplinary environment, Work autonomously, work in teams, Producing new research ideas, Promotion of free, creative and inductive thinking.
- Course content
-
Introduction to Numerical Methods, Introduction to R, Numerical Methods in R, Introduction to MATLAB, Numerical Methods in MATLAB, Arrays/Vectors in MATLAB/R, Plots in MATLAB/R, Solving algebraic equations (Bisection method, Newton’s Method, etc), Solving systems of algebraic equations (QR, Singular, Eigen, LU, etc), Systems of nonlinear equations, Numerical Differentiation and Integration, Optimization, Analyzing data, Fitting models to data, Interpolation.
- Recommended bibliography
-
- James P. Howard II, “Computational Methods for Numerical Analysis with R (Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series)”, 1st edition, Chapman and Hall/CRC, 2017, ISBN: 978-1498723633
- Owen Jones, Robert Maillarder, Andrew Robinson, “Introduction to Scientific Programming and Simulation Using R”, 2nd edition, Chapman and Hall/CRC, 2014, ISBN: 978-1466569997
- Alfio Quarteroni, Fausto Saleri, Paola Gervasio, “Scientific Computing with MATLAB and Octave”, 4th edition, Springer, 2014, ISBN: 978-3642453663
- Eihab B. M. Bashier, “Practical Numerical and Scientific Computing with MATLAB® and Python”, 1st edition, CRC Press, 2020, ISBN: 9780367076696
- S. D. Conte, Carl De Boor, “Elementary Numerical Analysis: An Algorithmic Approach, updated with MATLAB”, 1st edition, SIAM-Society for Industrial and Applied Mathematics, 2017, ISBN: 978-1611975192
- Nabil Nassif, Dolly K. Fayyad, “Introduction to Numerical Analysis and Scientific Computing”, 1st edition, Chapman and Hall/CRC, 2013, ISBN: 978-1466589483
- Victor A. Bloomfield, “Using R for Numerical Analysis in Science and Engineering”, 1st edition, Routledge, 2014, ISBN: 978-1439884485
Additional suggested bibliography:
-
Cymra Haskell, “Introduction to Scientific Programming with MATLAB”, 2010, available at https://dornsife.usc.edu/assets/sites/372/docs/Software_Resources/Matlab/cover.pdf
-
Cesar Lopez, “MATLAB Programming for Numerical Analysis (Matlab Solutions)”, 1st edition, Apress, 2014, ISBN: 978-1484202968