Dersin Bilgileri

Dersin Adı	Türkçe	Hesaplamalı Bilimler için İstatistiksel Veri Analizi
	İngilizce	Statistical Data Analysis for Computational Sciences
Dersin Kodu	MAT 555E	Kredi	Ders (saat/hafta)	Uygulama (saat/hafta)	Labratuvar (saat/hafta)
Dönem	-	3	3	-	-
Dersin Dili		İngilizce
Dersin Koordinatörü		Atabey Kaygun
Dersin Amaçları		This course is designed to provide necessary mathematical and computational background on statistical data analysis tools and algorithms needed by graduate students working in applied sciences.
Dersin Tanımı		The differences between statistics, machine learning and data science. Basic statistics and hypothesis testing. Optimization techniques. Least square regression. Regularization, ridge and lasso regression. Logistic and probit regression. PCA, LDA and SVM. Classification and clustering. K-NN and K-means algorithms. Entropy and gini. Decision trees and random forests. Bayes rule and naive Bayes algorithm. Perceptron. Computation graphs and artificial neural networks. Examples. Hopefield networks.
Dersin Çıktıları
Önkoşullar
Gereken Olanaklar
Diğer
Ders Kitabı		Ethem Alpaydın, "Introduction to Machine Learning." MIT Press. Stuart J. Russell and Peter Norvig, "Artificial Intelligence: A Modern Approach." Prentice Hall. Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, "Elements of Statistical Learning." Springer. Kevin P. Murphy, "Machine Learning: A Probabilistic Perspective." MIT Press. Jake VanderPlas, "Python Data Science Handbook."
Diğer Referanslar

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