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Course Description Course Name : Statistical Learning Methods and Pattern Recognition Course Code : EE-639 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. TURGAY İBRİKÇİ Learning Outcomes of the Course : Understands where Pattern Recognition sits in the hierarchy of artificial intelligence and soft computing techniques Develops expertise in various unsupervised learning algorithms such as clustering techniques (agglomerative, fuzzy, graph theory based, etc.), multivariate analysis approaches (PCA, MDS, LDA, etc.), image analysis (edge detection, etc.), as well as feature selection and generation. Has the ability to apply these techniques in exploratory data analysis. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : To have information about the implementation of statistical learning models for Pattern recognition problems, Course Contents : This course will cover several topics on pattern recognition (PR), artificial neural networks (ANN), and machine learning (ML). Applications in image analysis, target detection, optical character recognition, DNA sequence alignment, protein structure matching, data mining, network intrusion detection, engine trouble shooting.. 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, definition of pattern recognition Reading the related chapters of the book Presentation 2 Overview of supervised vs. unsupervised learning techniques Reading the related chapters of the book Presentation 3 Clustering overview (categories, proximity measures, etc.) Reading the related chapters of the book Presentation 4 Sequential clustering approaches Reading the related chapters of the book Presentation 5 Hierarchical clustering approaches Reading the related chapters of the book Presentation 6 Cost function optimization clustering approaches Reading the related chapters of the book Presentation 7 Probabilistic clustering approaches Reading the related chapters of the book Presentation 8 Midterm Exam İt cover previous topics. Midterm Exam 9 Cluster validity metrics Reading the related chapters of the book Presentation 10 Multivariate analysis techniques Reading the related chapters of the book Presentation 11 Principal Components Analysis Reading the related chapters of the book Presentation 12 Multidimensional scaling Reading the related chapters of the book Presentation 13 Linear Discriminant Analysis Reading the related chapters of the book Presentation 14 Image analysis techniques Reading the related chapters of the book Presentation 15 Feature selection and generation Reading the related chapters of the book Presentation 16/17 Final Exam Studying of all the topics Final Exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)   Required Course Material(s)  Sources of the Internet   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 20     Homeworks/Projects/Others 2 80 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 Communicates with people in an appropriate language and style. 3 2 Specializes by furthering his knowledge level at least in one of the basic subfields of electiral-electronic engineering. 4 3 Grasps the integrity formed by the topics involved in the field of specialization. 4 4 Grasps and follows the existing literature in the field of specialization. 4 5 Comprehends the interdisciplinary interaction of his field with other fields. 4 6 Has the aptitude to pursue theoretical and experimental work. 5 7 Forms a scientific text by compiling the knowledge obtained from research. 5 8 Works in a programmed manner within the framework set by the advisor on the thesis topic, in accordance with the logical integrity required by this topic. 4 9 Performs a literature search in scientific databases; in particular, to scan the databases in an appropriate manner, to list and categorize the listed items. 3 10 Has English capability at a level adequate to read and understand a scientific text in his field of specialization, written in English. 1 11 Compiles his/her knowledge in his/her field of specialization. in a presentation format, and presents in a clear and effective way. 4 12 Writes a computer code aimed at a specific purpose, in general, and related with his/her field of specialization, in particular 4 13 Pursues research ın new topics based on his/her existing research experıence. 4 14 Gives guidance in environments where problems related with his/her field need to be solved, and takes initiative. 3 15 Develops and evaluates projects, policies and processes in his field of specialization. 3 * 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 4 56 Assesment Related Works     Homeworks, Projects, Others 2 10 20     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 15 15 Total Workload: 143 Total Workload / 25 (h): 5.72 ECTS Credit: 6