Course Time: |
Wednesday, 09:55-12:30 |
Place: |
C208 |
Instructor: |
Assoc. Prof.Dr. Muhammet G. Cinsdikici, cinsdikici@gmail.com
Office Hours: Tuesday,13.30-15.30 Office Phone: |
Assistant(s): |
Office Phone: |
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: |
2018, Francois Chollet, “Deep Learning with Python“, Manning Publishing,ISBN 9781617294433
2017, Sebastian Raschka, “Python Machine Learning 2.nd edt”, PACKT Publishing, ISBN: 978-1-783-55513-0 2016-José Unpingco, “Python for Probability, Statistics, and Machine Learning“, Springer, ISBN 978-3-319-30715-2 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. |
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 |
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)
Lecture04 – Logistic Regression
Lecture05 – Regularization
Lecture06 – Neural Network Fundementals, Components
* Cinsdikici-Introduction Presentation
* Simon Haykin – Introduction to Neural Nets
* 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
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