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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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Intelligent system concept: Introduction
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R1: C1
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2
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Example for different application areas Intelligent system models
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R1: C2, R2: C2
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3
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Example for different application areas Intelligent system models
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R1: C2, R2: C2
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4
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Different intelligent system design and development algorithms
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R1: C2, R2: C2
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5
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Different intelligent system design and development algorithms
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R1: C2, R2: C2
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6
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Information flow, Derivation approach, Expert systems
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R1: C2, R2: C2
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7
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Expert Systems and Applications
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R1: C3
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8
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Hybrid Expert Systems and Applications
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R1: C3
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9
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Machine Learning (Data Mining, Neural Networks), Decision Support Systems and Derivation Algorithms and Applications (MATLAB, ...)
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R1: C4
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10
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Machine Learning (Data Mining, Neural Networks), Decision Support Systems and Derivation Algorithms and Applications (MATLAB, ...)
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R1: C4
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11
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Machine Learning (Data Mining, Neural Networks), Decision Support Systems and Derivation Algorithms and Applications (MATLAB, ...)
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R1: C4
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12
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Machine Learning (Data Mining, Neural Networks), Decision Support Systems and Derivation Algorithms and Applications (MATLAB, ...)
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R1: C4
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13
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Machine Learning (Data Mining, Neural Networks), Decision Support Systems and Derivation Algorithms and Applications (MATLAB, ...)
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R1: C4
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14
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Machine Learning (Data Mining, Neural Networks), Decision Support Systems and Derivation Algorithms and Applications (MATLAB, ...)
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R1: C4
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Prerequisites
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-
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Language of Instruction
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Turkish
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Responsible
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-
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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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R1. Stuart J. Russell and Peter Norvig, Artificial Intelligence: A Modern Approach,
Third Edition, 2010.
R2. Introduction to Data Mining P. N. Tan, M. Steinbach, V. Kumar.
Addison-Wesley, 2005.
R3. Data Mining: Practical Machine Learning Tools and Techniques (Third
Edition)". I. H. Witten, E. Frank, M. A. Hall, Morgan Kaufmann 2011.
R4. The Elements of Statistical Learning: data mining, inference and
prediction, T. Hastie, R. Tibshirani, and J. Friedman, Springer, 2001.
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Supplementary Book
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-
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Goals
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It is the design and development of new smart systems for use in different fields of study and applications in Chemical Engineering.
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Content
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Intelligent system concept: Introduction
Example Smart system models for different application areas
Different Intelligent system design and development algorithms
Information flow, Inference approach, Expert systems
Expert systems and Applications
Hybrid Expert Systems and Applications
Machine Learning (Data Mining), Neural Networks, Decision Support Systems and Inference algorithms and applications (MATLAB)
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Program Learning Outcomes |
Level of Contribution |
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1
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To be able to apply mathematics, science and engineering theories and principles to Chemical Engineering problems.
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4
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2
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To have the ability to define, model, and solve problems related to Chemical Engineering.
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4
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3
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To be able to design and conduct experiments, as well as to analyze and interpret data.
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4
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4
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To be able to design and analyze a process for a specific purpose within technical and economical limitations.
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3
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5
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To be able to use modern techniques and calculation tools required for engineering applications.
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3
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6
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To have the awareness of professional liabilities and ethics.
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-
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7
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To be able to get involved in interdisciplined and multidisciplined team work.
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3
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8
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To be able to declare his/her opinions orally or written in a clear, concise and brief manner.
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-
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9
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To improve him/herself by following the developments in science, technology, modern issues, and know the importance of lifelong learning.
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4
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10
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To be able to evaluate engineering solutions for the global and social problems especially for the health, safety, and environmental problems.
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-
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11
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To have knowledge about of contemporary issues.
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4
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