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Week
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Topics
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Study Metarials
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1
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Introduction to the course and different data types in GIS
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Reading related articles and lecture notes
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2
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Uncertainty and sources of error on GIS data
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Reading related articles and lecture notes
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3
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Overview of data mining (Classification / Clustering / Regression)
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Reading related articles and lecture notes
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4
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Supervised learning / unsupervised learning
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Reading related articles and lecture notes
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5
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Dimension reduction / Principal component analysis
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Reading related articles and lecture notes
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6
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Maximum Likelihood Analysis
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Reading related articles and lecture notes
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7
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k-Nearest neighborhood
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Reading related articles and lecture notes
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8
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Artificial Neural Networks
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Reading related articles and lecture notes
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9
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Decision Trees
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Reading related articles and lecture notes
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10
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Decision Trees
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Reading related articles and lecture notes
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11
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Support Vector Machines
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Reading related articles and lecture notes
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12
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Nonparametric regression splines
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Reading related articles and lecture notes
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13
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Nonparametric regression splines
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Reading related articles and lecture notes
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14
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Model evaluation
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Reading related articles and lecture notes
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Prerequisites
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Successful completion of an entry-level GIS and statistics course
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Language of Instruction
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Turkish
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Responsible
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Yrd. Doç. Dr. Semih KUTER
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Instructors
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-
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Assistants
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-
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Resources
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1. Wing, M. G., & Bettinger, P. (2008). Geographic Information Systems: Applications in Natural Resource Management (2nd ed.): Oxford University Press.
2. Liu, J. G., & Mason, P. (2009). Essential image processing and GIS for remote sensing: John Wiley & Sons.
3. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). NY, USA: Springer.
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Supplementary Book
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-
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Goals
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Data mining is basically a process of extracting information from a data set and converting it into an understandable structure for later analysis. The aim of this course is to introduce basic data pre-processing, data mining and statistical learning methods to different GIS data.
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Content
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-
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Program Learning Outcomes |
Level of Contribution |
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1
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Must learn the methods of both improving the basic sciences and engineering knowledge and obtaining new knowledges at a level of expertise
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4
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2
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Must be able to design, develop, and apply methods and experiments at advanced level to solve forestry problems, and analyses and interpret their results
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4
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3
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Must be able to provide solutions for the country?s forestry and environmental problems by considering global, public and ecosystem conditions
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-
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4
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Must be able to setup interdisciplinary approach to reach an advanced solution for forestry problems
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4
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5
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Must be able to act in an advanced level of professional ethics and responsibility during the identification and resolution of problems encountered in forestry
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-
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6
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Must be able to do the task in a single or multi-disciplinary working groups, and be able to show effective communication
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4
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7
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Must have the ability to effective use of both information technologies and a foreign language at an advanced level
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4
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8
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Must be able to describe, foresee and solve the current problems in the fields of forestry and other related problems at advanced level brought by current global developments
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3
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9
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Must be able to use the tools and techniques required for forestry applications at an advanced level
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4
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10
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Must be able to think, interpret, analyse and synthesize forestry practices at an advanced level by using a three dimensional perspective
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3
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11
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Must be able to research and survey any kinds of natural resources and event, and write advanced reliable reports by using the achieved findings
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3
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12
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Must be able to understand the necessity of life-long learning at an advanced level, and to be able to use the methods that keeps obtained knowledge up date
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3
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