Data Modelling and Analysis
Geodesy and Geoinformation Engineering
Institute of Natural and Applied Sciences
Third Cycle (Doctorate Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Data Modelling and Analysis | JJM615 | Compulsory | Doctorate degree | 1 | Fall | 10 |
Name of Lecturer(s)
Associate Prof. Dr. Orhan KURT
Learning Outcomes of the Course Unit
1) To comprehend the parametric and the non-parametric data.
2) To use the fundamental probability and statistic rules.
3) To use the fundamental numerical analysis rules.
4) To set hypothesis tests. To test experimental results.
5) To solve and test any parametric model.
6) To determine a non-parametric model related data and to interpret its result.
7) To use soft computing methods.
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||
| 1 | 2 | 3 | 4 | ||
| Learning Outcomes | |||||
| 1 | High | Middle | Low | Middle | |
| 2 | High | Middle | Middle | Middle | |
| 3 | High | Middle | Low | Middle | |
| 4 | High | Middle | Middle | Middle | |
| 5 | High | Middle | Middle | Middle | |
| 6 | High | Low | Middle | Middle | |
| 7 | High | Low | Middle | Middle | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Probability and statistic, numerical analysis, programming.
Course Contents
Parametric and non-parametric experimental data. Normal and test distributions. Taylor expansion and linear algebra. Criterion functions (Lp-norm). Lagrange criterion function. Solutions of linear equation systems. Hypothesis tests. The first and second type of errors. Soft computing methods. Fuzzy Logic. Artificial Neural Network. Artificial Intelligent. Genetic algorithm.
Weekly Schedule
1) Parametric and non-parametric experimental data.2) Normal distribution. Test distributions.
3) Taylor expansion. Linear algebra.
4) Criterion functions (Lp-norm). L1-norm. Lmax-norm.
5) L2-norm. Lagrange criterion function.
6) Solutions of linear equation systems.
7) Parametric modeling, its solution and analysis.
8) HOMEWORK-I Modeling and analyzing a parametric data set in a free compiler.
9) Hypothesis tests. First and second type errors in the statistical tests.
10) Soft computing methods. Fuzzy logic.
11) Artificial Neural Networks. Deep Learning.
12) Machine Learning. Artificial Intelligence.
13) Genetic algorithm.
14) Modeling and analysis of non-parametric data set.
15) HOMEWORK-II Modeling and analyzing a non-parametric data set in a free compiler.
16) FINAL exam.
Recommended or Required Reading
1- William H. PRESS, Brain P. FLANNERY, Saul A. TEUKOSLSKY, William T. VETTERLING (2002), Numerical Recipes in C, Second Edition, Cambridge University Press, Cambridge UK. http://numerical.recipes/, (Accessed: 04 Aug 2019).
2- Jaan KIUSALAAS (2013), Numerical Methods in Engineering with Python 3, Cambridge, www.cambridge.org/9780521852876, (Accessed: 04 Aug 2019).
3- Orhan KURT (2010), Numerical Analysis, Lecture Notes, Kocaeli University, Kocaeli. https://orhankurt.jimdo.com/undergraduate/spring-bahar/muh208-numerical-analysis/, (Accessed: 04 Aug 2019).
4- Karl-Rudolf KOCH (1999), Parameter Estimation and Hypothesis Testing in Linear Models, Springer-Verlag Berlin Heidelberg New York, ISBN-540-65257-4. https://link.springer.com/book/10.1007/978-3-662-03976-2, (Accessed: 04 Aug 2019).
5- Orhan KURT (2011), Analysis of Geodetic Data, Lecture Notes, Kocaeli University, https://orhankurt.jimdo.com/graduate/autumn/jjm513-estimation-of-geodetic-quantities-and-hypothesis-testing-in-linear-models/, (Accessed: 04 Aug 2019).
6- Orhan KURT (2013), Advanced Adjustment, Lecture Notes, Kocaeli University, https://orhankurt.jimdo.com/undergraduate/autumn-g%C3%Bcz/hrt409-advanced-topics-in-adjustment/, (Accessed: 04 Aug 2019).
7- Anil K. JAIN, Jianchang MAO, K. MOHIUDDIN (1996), Artifical Neural Networks, IEEE Computer Special Issue on Neural Computing, March 1996. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=485891, (Accessed: 04 Aug 2019).
8- Orhan AKYILMAZ (2005), Applications of Soft Computing Methods in Geodesy, İTÜ-FBE, Ph.D. Thesis, https://polen.itu.edu.tr/bitstream/11527/12127/1/2851.pdf, (Accessed: 04 Aug 2019).
9- Michael Nielsen (2015), Neural Networks and Deep Learning, Determination Press, http://static.latexstudio.net/article/2018/0912/neuralnetworksanddeeplearning.pdf, (Accessed: 04 Aug 2019).
10- Andrej Krenker, Janez Bes?ter and Andrej Kos (2011). Introduction to the Artificial Neural Networks, ArtificialNeural Networks - Methodological Advances and Biomedical Applications, Prof. Kenji Suzuki (Ed.), ISBN: 978-953-307-243-2, InTech, Available from: http://www.intechopen.com/books/artificial-neural-networks-methodological-advances-and-biomedical-applications/introduction-to-the-artificial-neural-networks, (Accessed: 04 Aug 2019).
11- Fakhreddine O. Karray and Clarence de Silva (2004), Soft Computing and Intelligent Systems Design, Pearson Education Limited 2004, SBN 0 321 11617 8.
12- Darrell Whitley (1994), A genetic algorithm tutorial, Statistics and Computing (1994) 4, 65-85, https://link.springer.com/content/pdf/10.1007%2FBF00175354.pdf, (Accessed: 04 Aug 2019).
13- John LeFlohic (1999), Genetic Algorithms Tutorials (in C), http://www-cs-students.stanford.edu/~jl/Essays/ga.html, (Accessed: 04 Aug 2019).
14- Eric Stoltz (2018), Evolution of a salesman: A complete genetic algorithm tutorial for Python, https://towardsdatascience.com/evolution-of-a-salesman-a-complete-genetic-algorithm-tutorial-for-python-6fe5d2b3ca35, (Accessed: 04 Aug 2019).
15- Salvatore Mangano (1995), Genetic Algorithm, http://www.javamath.com/snucode/translateit.pdf, (Accessed: 04 Aug 2019).
16- Serge Guillaume (2001), Designing Fuzzy Inference Systems from Data: An Interpretability-Oriented Review, IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 9, NO. 3, JUNE 2001, https://pdfs.semanticscholar.org/fdf1/0b28242cf1e814052911e9c77b5a3d34248a.pdf, (Accessed: 04 Aug 2019).
17- Chonghua Wang (2015), A Study of Membership Functions on Mamdani-Type Fuzzy Inference System for Industrial Decision-Making, Theses, and Dissertations. Paper 1665. https://preserve.lehigh.edu/cgi/viewcontent.cgi?article=2665
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Drill and Practice
4) Self Study
5) Problem Solving
Assessment Methods and Criteria
Contribution of Quiz to Course Grade |
70% |
|---|---|
Contribution of Final Examination to Course Grade |
30% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required