Course Information

Course Name	Turkish	Matematik Mühendisliğinde Yapay Zeka
	English	Artificial Intelligence in Mathematical Engineering
Course Code	MYZ 309E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	-	3	3	-	-
Course Language		English
Course Coordinator		Gül İnan Gül İnan
Course Objectives		This course aims to: I. Provide a foundational understanding of commonly used statistical learning methods for inference and prediction in data analysis, II. Cover both supervised learning (regression and classification) and unsupervised learning (clustering) techniques, and III. Facilitate the practical application of these learning methods to real-world data analysis problems using Python. IV. Equip students with essential data analysis skills while enhancing their critical thinking and problem-solving abilities, and develop their analytical report writing skills.
Course Description		Best practices in data analysis. Statistics, data science, machine learning, and artificial intelligence: similarities and differences. Differences between supervised and unsupervised learning algorithms. Linear regression, regularization methods (Lasso and Ridge), classification techniques (logistic regression, support vector machines, tree-based methods), model evaluation strategies (cross-validation and performance metrics). Dimensionality reduction and principal component analysis. Clustering algorithms. Artificial neural networks. Applications of artificial intelligence.
Course Outcomes		Students completing this course will be able to: I. Develop a foundational understanding of key artificial intelligence challenges in data analysis, II. Acquire practical skills in utilizing fundamental statistical and machine learning tools for solving artificial intelligence problems, and III. Write clear and concise analytical reports on the machine learning and statistical models they create during the course.
Pre-requisite(s)
Required Facilities
Other
Textbook
Other References		Hastie, T., Tibshirani, R., Friedman, J.H., and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021). An Introduction to Statistical Learning: With Applications in R. New York: Springer. Müller, A.C., and Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media, Inc. Deisenroth, M.P., Faisal, A.A., and Ong, C. S. (2020). Mathematics for Machine Learning. Cambridge University Press.

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