New Mathematics Courses
MATH 363 Stochastic Modelling
3 Credits, 7 ECTS | Elective Course | Fall 2026
Stochastic modelling is an important field of applied mathematics that encompasses fundamental methods for mathematically describing, analysing, and interpreting systems involving randomness.
The course covers probability theory, Poisson processes, Markov chains, random walks, queueing theory, financial and actuarial modelling, the Wiener process, and the Black–Scholes option pricing model. In addition to the theoretical foundations, applications of these methods in finance, insurance, and other quantitative fields are examined.
Yeditepe University
Faculty of Engineering and Natural Sciences
Department of Mathematics
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MATH 348 Mathematical Modelling
3 Credits, 7 ECTS | Elective Course | Spring 2027
The analysis and solution of real-life problems through mathematical methods constitute one of the fundamental areas of applied mathematics.
This course focuses on transforming real-world systems into mathematical models, analysing these models using analytical and numerical methods, and interpreting the resulting outcomes. The course covers difference equations, differential equations, linear and nonlinear models, Markov chains, stability analysis, and perturbation methods. The mathematical modelling approach is also examined through examples from population dynamics, finance, epidemiology, and industrial applications.
Yeditepe University
Faculty of Engineering and Natural Sciences
Department of Mathematics
#YeditepeUniversity #Yeditepe30 #YeditepeMathematics #MATH348 #MathematicalModelling #AppliedMathematics #DifferentialEquations #MarkovChains #MathematicalModeling
MATH 463 Data Science and Statistical Learning
3 Credits, 7 ECTS | Elective Course | Spring 2027
Data science and statistical learning encompass fundamental methods that enable meaningful information to be extracted from large datasets. Today, these methods are widely used in numerous fields, particularly artificial intelligence, finance, healthcare, engineering, and technology.
The course covers data collection, data cleaning, exploratory data analysis, probability distributions, and fundamental statistical methods. Topics include linear and logistic regression, decision trees, random forests, support vector machines (SVM), artificial neural networks and deep learning, clustering algorithms, dimensionality reduction methods, and model evaluation techniques. Data analysis and statistical learning applications are also carried out using the R programming language.
Yeditepe University
Faculty of Engineering and Natural Sciences
Department of Mathematics
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