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Course Description Course Name : Intelligent Optimization Techniques Course Code : CENG-568 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Spring (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. UMUTORHAN Learning Outcomes of the Course : Knows mathematical fundamentals of optimization techniques used in artificial intelligence. Interprets a dataset by applying classification and clustering methods Understands a new intelligent optimization technique by reading a study Revises a known technique according to the problem. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : In this course, optimization basis of artificial intelligent algorithms like genetic algorithms, artificial neural networks, ant colony algorithm, support vector machine, search and the applications on their solutions is aimed. Course Contents : Introduction, constrains describing, least squares optimization, conventional search methods and tabu search, direct search and genetic algorithms, simulated annealing, ant colony optimization and swarm intelligence, levenberg-marquartd algorithm, non-linear programming and support vector machines, entropy based optimization, optimization in cluster analysis, optimization by principle component analysis, Matlab applications and samples. Language of Instruction : English Work Place : Classroom   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Introduction to optimization and machine learning Reading corresponding subject in lecture notes Expression and Applications with MATLAB 2 K-Means Clustering, Classification by K-Nearest Neighbor Reading corresponding subject in lecture notes Expression and Applications with MATLAB 3 Entropy, Decision Tree Learning, ID3 and C4.5 algorithms Reading corresponding subject in lecture notes Expression and Applications with MATLAB 4 Probability and Conditional Probability, Bayesian Theorem, Naive Bayes Reading corresponding subject in lecture notes Expression and Applications with MATLAB 5 Least squares optimization and linear regression Reading corresponding subject in lecture notes Expression and Applications with MATLAB 6 Introduction to Artificial Neural Networks, Perceptron, Adaline, Least Mean Squares Reading corresponding subject in lecture notes Expression and Applications with MATLAB 7 Midterm exam Preparation for the exam Written Exam 8 Levenberg- Marquartd algorithm and artificial neural networks Reading corresponding subject in lecture notes Expression and Applications with MATLAB 9 Reinforcement Learning, Q-Learning, TD-Learning, Learning Vector Quantization Network, LVQ2, LVQ-X Reading corresponding subject in lecture notes Expression and Applications with MATLAB 10 Mapping and Kernel Functions, Radial Basis Function (RBF) Network Reading corresponding subject in lecture notes Expression and Applications with MATLAB 11 Optimization by Lagrange Method, Support Vector Machine Reading corresponding subject in lecture notes Expression and Applications with MATLAB 12 Dimension Reduction, Principal Component Analysis, Linear Discriminant Analysis Reading corresponding subject in lecture notes Expression and Applications with MATLAB 13 Matlab applications and samples Reading corresponding subject in lecture notes Applications with MATLAB 14 Matlab applications and samples Reading corresponding subject in lecture notes Applications with MATLAB 15 Project Presentation Preparing a presentation about the given subject Student oral presentations 16/17 Final exam Preparation for the exam Written Exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  How to Solve It: Modern Heuristics, Z. Michalewicz, D. B. Fogel, Springer, 2004. Intelligent Optimization Techniques, D.T. Pham, D. Karaboga, Springer, 1999. Pattern Recognition and Machine Learning, C. M. Bishop, Springer, 2007. Neural Networks and Learning Machines, S. Haykin, Prentice Hall, 2008. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 50     Homeworks/Projects/Others 2 50 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Reaches wide and deep knowledge through scientific research in the field of computer engineering, evaluates, implements, and comments. 5 2 Describes and uses information hidden in limited or missing data in the field of computer engineering by using scientific methods and integrates it with information from various disciplines. 5 3 Follows new and emerging applications of computer engineering profession, if necessary, examines and learns them 4 4 Develops methods and applies innovative approaches in order to formulate and solve problems in computer engineering. 5 5 Proposes new and/or original ideas and methods in the field of computer engineering in developing innovative solutions for designing systems, components or processes. 4 6 Designs and implements analytical modeling and experimental research and solves the complex situations encountered in this process in the field of Computer Engineering 5 7 works in multi disciplinary teams and takes a leading role and responsibility. 1 8 Learns at least one foreign language at the European Language Portfolio B2 level to communicate orally and written 1 9 Presents his/her research findings systematically and clearly in oral and written forms in national and international meetings. 3 10 Describes social and environmental implications of engineering practice. 1 11 Considers social, scientific and ethical values in collection, interpretation and announcement of data. 3 12 Acquires a comprehensive knowledge about methods and tools of computer engineering and their limitations. 5 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 Assesment Related Works     Homeworks, Projects, Others 2 15 30     Mid-term Exams (Written, Oral, etc.) 1 16 16     Final Exam 1 20 20 Total Workload: 150 Total Workload / 25 (h): 6 ECTS Credit: 6