UBI 521 – Machine Learning (Fall, 2016)

Course Time:	Wednesday, 13:15-16:15
Place:	UBE, Room 103
Instructor:	Assoc.Prof.Dr.Muhammed G. Cinsdikici, cinsdikici@gmail.com Office Hours: Wednesday,13.30-15.30 Office Phone: 3205 / 0(232)311 3205
Assistant(s):	Cemre Candemir / Dr.Kaya Oğuz (Office Phone:3242 / 0(232)311 3242)
Class Group	Ube ML Class Google Group
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:	Matlab or one of the Programing languages (Java, C#), Statistics & Mathematics background for engineering departments
Textbooks:	2015, Sebastian Raschka, “Python Machine Learning”, 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 2007, 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.
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. Matlab 2011b 2. Python 2.7 + Machine Learning Libraries 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

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

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