Biyomedikal Mühendisliği

CE 395 | Ders Tanıtım Bilgileri

Dersin Adı

Special Topics in Machine Learning

Kodu	Yarıyıl	Teori (saat/hafta)	Uygulama/Lab (saat/hafta)	Yerel Kredi	AKTS
CE 395	SPRING	3	0	3	5

Ön-Koşul(lar)	None
Dersin Dili	English
Dersin Türü	ELECTIVE_COURSE
Dersin Düzeyi	Lisans
Dersin Veriliş Şekli
Dersin Öğretim Yöntem ve Teknikleri	Problem solving Lecture / Presentation
Ulusal Meslek Sınıflandırma Kodu	-
Dersin Koordinatörü	Dr. Öğr. Üyesi Kaya Oğuz
Öğretim Eleman(lar)ı	-
Yardımcı(ları)	-

Dersin Amacı

This course provides the mathematical and conceptual foundations for advanced machine learning methods. It covers sampling and information theory, digital filtering and the discrete Fourier transform, vector and matrix manipulations, numerical optimization, and the foundations of statistical learning theory.

Öğrenme Çıktıları

Bu dersi başarıyla tamamlayabilen öğrenciler;

Ad	Açıklama	PC Alt	* Katkı Düzeyi
Ad	Açıklama	PC Alt	1	2	3	4	5
LO1	Be able to apply signal sampling and filtering principles by being involved in data processing algorithms.			X
LO2	Be able to use vectors and matrices in data processing algorithms.				X
LO3	Be able to use numerical optimization methods for machine learning algorithms.				X
LO4	Describe the principles of statistical learning theory.			X
LO5	Compare machine learning algorithms in terms of bias-variance trade-off, model complexity, and cross-validation.					X

Ders Tanımı

The following topics will be included in the syllabus: sampling and information theory, digital filtering and the discrete Fourier transform, basic vector and matrix operations, fundamentals of numerical optimization, fundamentals of statistical learning theory.

Dersin İlişkili Olduğu Sürdürülebilir Kalkınma Amaçları

Dersin Kategorisi	Temel Ders
	Uzmanlık/Alan Dersleri	X
	Destek Dersleri
	İletişim ve Yönetim Becerileri Dersleri
	Aktarılabilir Beceri Dersleri

HAFTALIK KONULAR VE İLGİLİ ÖN HAZIRLIK ÇALIŞMALARI

Hafta	Konular	Ön Hazırlık	Öğrenme Çıktısı
1	Introduction: What is machine learning?	Chapter 1. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning. ISBN 9780387216065	45fdb90f
2	Signal sampling fundamentals - sampling frequency, Nyquist frequency, signal and image resolution, Shannon information theory, efficient codes, data compression	Chapter 1. Signals & Systems. Oppenheim & Willsky. ISBN 0136511759.	2da6abf8
3	Introduction to digital filtering, convolution, linear and time invariant system theory, 1D and 2D filters, linear and nonlinear filters	Chapter 2. Signals & Systems. Oppenheim & Willsky. ISBN 0136511759.	2da6abf8
4	Fourier transform, discrete Fourier transform, spectrum of signal and image, complex numbers	Chapter 3. Signals & Systems. Oppenheim & Willsky. ISBN 0136511759.	2da6abf8
5	Summary of linear algebra - row and column vectors, matrices, matrix multiplication, the exclusion product, norm	Linear Algebra and Its Applications, David C. Lay, Steven R. Lay, Judi J. McDonald, Pearson, 5th Edition	6ee14736
6	Fundamentals of numerical optimization – optimality conditions, KKT conditions, slope descent optimization, convex optimization programs	Chapter 1. Part 1.1-1.4. Chapter 4. Part 4.3, 4.4. Nonlinear Programming, D. Bertsekas, Athena Scientific, 3rd Edition	fc82ce4f
7	Midterm exam		6ee14736
8	Primal-dual theory, large-scale optimization, stochastic gradient descent method	Chapter 2. Chapter 6. Part 6.1-6.4. Nonlinear Programming, D. Bertsekas, Athena Scientific, 3rd Edition	fc82ce4f
9	Summary of probability, random variables and probability distributions, Bayes' theorem, expected values, Law of Large Numbers, Central Limit Theorem, Markov, Jensen, Chernoff and Hoeffding inequalities	Statistics for Engineers and Scientists, William Navidi, 4th Ed., Mc-Graw Hill.	45fdb90f
10	Introduction to statistical learning theory - learning as a statistical activity, supervised and unsupervised learning, regression and classification	Chapter 2. Part 2.1-2.3. The Elements of Statistical Learning, T. Hastie, R. Tibshirani, J. Friedman, ISBN 9780387216065	45fdb90f
11	Statistical decision theory, function estimation, statistical models, restricted estimators, curse of dimensionality, bias-variance trade-off	Chapter 2. Part 2.4-2.6, 2.8. Chapter 7. Part 7.2. The Elements of Statistical Learning, T. Hastie, R. Tibshirani, J. Friedman, ISBN 9780387216065	9a3a15f2
12	Model evaluation and selection, effective model sizes, AIC, BIC, Vapnik-Chervonenkis size	Chapter 7. Part 7.2-7.7. The Elements of Statistical Learning, T. Hastie, R. Tibshirani, J. Friedman, ISBN 9780387216065	9a3a15f2
13	Vapnik-Chervonenkis dimension, cross-validation and its properties, bootstrap methods	Chapter 7. Part 7.9-7.11. The Elements of Statistical Learning, T. Hastie, R. Tibshirani, J. Friedman, ISBN 9780387216065	9a3a15f2
14	Review of the semester		2da6abf8
15	Review of the semester		fc82ce4f
16	Final exam		9a3a15f2

Ders Kitabı

A. Oppenheim; A. Willsky; Signals & Systems; Pearson; 1996; ISBN 0136511759

Önerilen Okumalar/Materyaller

D. Lay; S. Lay; J. McDonald; Linear Algebra and Its Applications; Pearson; 5th Edition; 2015; ISBN 9780321982384
D. Bertsekas; Nonlinear Programming; Athena Scientific; 3rd Edition; 2016; ISBN 9781886529052
W. Navidi; Statistics for Engineers and Scientists; Mc-Graw Hill; 3rd Edition; 2010; ISBN 9780073376332
T. Hastie; R. Tibshirani; J. Friedman; The Elements of Statistical Learning; Springer; 2013; ISBN 9780387216065.

DEĞERLENDİRME ÖLÇÜTLERİ

Yarıyıl Aktiviteleri	Sayı	Katkı Payı %	LO1	LO2	LO3	LO4	LO5
Ödev	5	30	X	X	X	X	X
Ara Sınav	1	30	X	X	X
Final Sınavı	1	40			X	X	X
Toplam	7	100

AKTS / İŞ YÜKÜ TABLOSU

Yarıyıl Aktiviteleri	Sayı	Süre (Saat)	İş Yükü
Katılım	-	-	-
Teorik Ders Saati	16	3	48
Laboratuvar / Uygulama Ders Saati	-	-	-
Sınıf Dışı Ders Çalışması	14	2	28
Arazi Çalışması	-	-	-
Küçük Sınav / Stüdyo Kritiği	-	-	-
Portfolyo	-	-	-
Ödev	5	6	30
Sunum / Jüri Önünde Sunum	-	-	-
Proje	-	-	-
Seminer/Çalıştay	-	-	-
Sözlü Sınav	-	-	-
Ara Sınavlar	1	20	20
Final Sınavı	1	24	24
		Toplam	150