CSE3220 – Machine Learning (Spring, 2022)

Course Time:	Tuesday, 09:55-12:30 / 17:00-19:30
Place:	C208
Instructor:	Assoc. Prof.Dr. Muhammet G. Cinsdikici, cinsdikici@{gmail.com, comvislab.com} Office Hours: Tuesday,13.30-15.30 Office Phone: 2107
Assistant(s):	Ahmet Cahit Yaşa
Class Group	Computer Engineering ML Class Google Group / MLClass@classroom.google.com
Description:	Machine Learning Introduction, Regression, Multivariate Regression, Gradient Descent Learning, Logistic Regression, Regularization, Artficial neural networks, biological foundations, cognitive processes and their modeling using artificial neural networks, problem solving using neural networks, supervised/unsupervised learning, Support Vector Machines, Principle Component Analysis, Naive Bayesian Learning.
Prerequisites:	Python or Matlab or one of the Programing languages (Java, C#), Statistics & Mathematics background for engineering departments
Textbooks:	2021, Zhi-Hu Zhou, “Machine Learning“, Springer, ISBN: 9789811519673 2021, Chris Mattmann, “Machine Learning with Tensorflow “, Manning, ISBN: 9781617297717 2021, Francois Chollet, “Deep Learning with Python“, Manning Publishing, ISBN: 9781617294433 2018, Birol Kuyumcu, “OpenCv Görüntü İşleme ve Yapay Öğrenme“, Level Kitap,  ISBN: 9786056567933 2017, Sebastian Raschka, “Python Machine Learning 2.nd edt”, PACKT Publishing, ISBN: 978-1-783-55513-0 2014, Ethem Alpaydin, “Introduction to Machine Learning – 3rd edt“, MIT Press, ISBN: 978-0-262-02818-9 2012, Kevin P. Murphy, “Machine Learning”, MIT Press, ISBN: 978-0-262-01802-9 2009, Simon Haykin, “Neural Networks and Learning Machines – 3rd edt”, Prentice Hall, ISBN: 978-0-131-47139-9 2006, Christopher Bishop, “Pattern Recognition and Machine Learning”, Springer, ISBN: 978-0-387-31073-2 2004, Mehmet Ö. Efe, “Artificial Neural Networks and Their Applications”, Boğaziçi University Publications, ISBN: 975-5-18223-3 1994, Laurene Fausett, “Fundementals of Neural Networks”, Prentice Hall, ISBN: 978-0-133-34186-7. Free Machine Learning with Matlab
Projects	All Projects are due at the beginning of class. Due dates for projects will be announced at least a week ahead of time. No late submission will be accepted!.
Tests:	Students will have one final exam. Students have to submit their final Technical Report to any journal in SCI or SCI-Expanded or conference in related area.
Grading:	%30 MidTerm + %20 Homeworks + %25 FinalExam + %25 Final Project
Late Submission:	Late submission of projects will not be accepted.
Programming Platforms/Tools:	1. Python 3.5 (or newer versions) + Machine Learning Libraries 2. Matlab R2018b and Simulink Bundled Student Edition 3. Visiual Studio 2015  + {OpenCV / Accord.NET etc..}
Suplimentary Best Video Lectures:	1. Andrew NG – Machine Learning – Stanford 2. Yaser Abu Mostafa – Machine Learning – Caltech 3. Patrick Winston – Artificial Intelligence – MIT 4. Emily Fox – Machine Learning

Lectures:

Lecture01 –  Introduction to Machine Learning
* Introduction to Machine Learning
Lecture02 –  Linear Regression with One Variable
* Linear Regression with One Variable
* Linear Algebra Background
Lecture03 – Linear Regression with Multiple Variables
* Linear Regression with Multiple Variables
* Matlab Scripting Backgound (Optional: Python) 
* Matrix Tutorial (Option)
Lecture04 – Logistic Regression
* Logistic Regression
Lecture05 – Regularization
* Regularization
Lecture06 – Neural Network Fundementals, Components
* Cinsdikici-Introduction Presentation
* Simon Haykin – Introduction to Neural Nets
* Simon Haykin – Perceptron
* Neural Nets Presentation
* Prof. Leslie Smith – Introduction to Neural Network
* Christos Stergiou and Dimitrios Siganos – Introduction to Neural Networks

* Genevieve Orr – Motivation for Neural Networks
* Learning Artificial Neural Networks [Robotics]
* Continuous_Mathematic_Notes.pdf
* Perceptron – Applet Demo
Lecture07 – Neural Network Models
*BackPropagation
* Khonen’s {SOM / LVQ 1, 2, 3}
Lecture08 – NeoCognitron – “Convolutional Neural Net” Deep Learning Model
* Cinsdikici – Neocognitron Variants/LeNet5
Lecture09 – SVM – Support Vector Machines
Lecture10 – Clustering

Lecture11 – Dimensionality Reduction (PCA / LDA)
Lecture12 – Speech Recognition
* AquaPhoenix – Audio Processing with Matlab
* Bern Plannerer – Speech Recognition with Matlab
* Ian McLoughlin – Applied Speech and Audio Processing with Matlab
* David Roberts – Voice Recognition Project
HomeWorks & Project:
HomeWork#1 – Image Segments Classification with Logistic Regression