Course Information

Course Name	Turkish	Veri Analizi ve Makine Öğrenmesinde Matematiksel Yöntemler
	English	Mathematical Methods in Data Analysis & Machine Learning
Course Code	HBM 538E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	1	3	3	-	-
Course Language		English
Course Coordinator		Süha Tuna Süha Tuna
Course Objectives		1. To teach mathematical backgrounds of data analysis and machine learning methods. 2. To perform computational analysis of data analysis and machine learning algorithms. 3. To select the appropriate method for the given problem and apply this computational method in a computer environment efficiently. 4. To examine and compare the results elicited from the computational methods
Course Description		Matrix spaces, matrix factorization, eigenvalues and eigenvectors, singular value decomposition, Eckart-Young Theorem, vector and matrix norms, principal component analysis, least squares method, linear equation systems, exponential matrices, derivatives of matrices, saddle points, minmax problem, function minimization, gradient descent method, stochastic gradient descent method, artificial neural networks, back-propagation algorithm, partial derivatives, convolutional neural networks, learning function, finding clusters in graphs
Course Outcomes		M.Sc. students who successfully pass this course gain knowledge, skills and competency in the following subjects; 1. Be able to understand the current data analysis and machine learning methods 2. Be able to apply the knowledge on vectors, matrices and linear equation systems in data analysis and machine learning 3. Be able to understand the optimization-based methods in data analysis and machine learning 4. Be able to perform computational analyses for the algorithms such as efficiency, stability and robustness 5. Be able to select and apply the appropriate and efficient technique for a given problem 6. Be able to justify the obtained results and understand the reasons for possible unfavourable outcomes
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Textbook		1) Gilbert Strang, (2019) “Linear Algebra and Learning from Data”, Wellesley-Cambridge Press. 2) W. Martinez, A. Martinez, J. Sofka (2010) “Exploratory Data Analysis with MATLAB”, Chapman & Hall CRC Press. 3) Christopher M. Bishop (2006) “Pattern Recognition and Machine Learning”, Springer. 4) Slawomir Kuziel, Xin-She Yang (2011) “Computational Optimization, Methods and Algorithms”, Springer. 5) Wei Qi Yan, (2021) “Computational Methods for Learning”, Springer
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