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Course Description Course Name : Introduction to Artificial Intelligence Course Code : EE-587 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 : Identifies the type of an AI problem (search, inference, decision making under uncertainty, game theory, etc). Formulates the problem as a particular type. (Example: define a state space for a search problem) Compares the difficulty of different versions of AI problems, in terms of computational complexity and the efficiency of existing algorithms. Implements, evaluates and compares the performance of various AI algorithms. Evaluation could include empirical demonstration or theoretical proofs. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : This course is about the theory and practice of Artificial Intelligence. We will study modern techniques for computers to represent task-relevant information and make intelligent (i.e. satisfying or optimal) decisions towards the achievement of goals. Course Contents : We will investigate questions about AI systems such as: how to represent knowledge, how to effectively generate appropriate sequences of actions and how to search among alternatives to find optimal or near-optimal solutions. We will also explore how to deal with uncertainty in the world, how to learn from experience, and how to learn decision rules from data. We expect that by the end of the course students will have a thorough understanding of the algorithmic foundations of AI, how probability and AI are closely interrelated, and how automated agents learn. 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, intelligent agents, captchas, problem solving as search Reading of related chapters of the book Presentation 2 Uninformed search Reading of related chapters of the book Presentation 3 Informed (heuristic) search Reading of related chapters of the book Presentation 4 Local search: Hill-climbing, simulated annealing, genetic algorithms Reading of related chapters of the book Presentation 5 Game playing, constraint satisfaction Reading of related chapters of the book Presentation 6 Machine learning: Neural networks, support vector machines Reading of related chapters of the book Presentation 7 Machine learning: Decision trees Reading of related chapters of the book Presentation 8 Midterm Exam İt covers all previous topics Midterm Exam 9 Probabilistic reasoning: Uncertainty Reading of related chapters of the book Presentation 10 Probabilistic reasoning: Bayesian networks Reading of related chapters of the book Presentation 11 Bayesian networks Reading of related chapters of the book Presentation 12 Applicationf of AI´s I Reading of related chapters of the book Presentation 13 Applicationf of AI´s II Reading of related chapters of the book Presentation 14 Presentations of of Students I Reading of related chapters of the book Presentation 15 Presentations of of Students II Reading of related chapters of the book Presentation 16/17 Final Exam It covers all topics in this course Final Exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Artificial Intelligence: A Modern Approach, 3rd edition, S. Russell and P. Norvig, Prentice Hall, Upper Saddle River, N.J., 2010 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. 1 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. 3 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. 4 7 Forms a scientific text by compiling the knowledge obtained from research. 3 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. 4 10 Has English capability at a level adequate to read and understand a scientific text in his field of specialization, written in English. 3 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. 3 14 Gives guidance in environments where problems related with his/her field need to be solved, and takes initiative. 4 15 Develops and evaluates projects, policies and processes in his field of specialization. 4 * 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 20 20 Total Workload: 148 Total Workload / 25 (h): 5.92 ECTS Credit: 6