Course Language:
İngilizce
Course Objectives:
The goal of this course is to provide students with a survey of different aspects of Artificial Intelligence (AI).
Course Content:
Introduction, programming language: LISP: array, tree, heap, queue and table structures, information display: production rules including hierarchies, propositional account, inference rules, frames, semantic networks, restrictions and systematical approaches, search, hypothesis and testing, depth first search, width first search, intuitional search, optimal search, game trees and reflexive search, mini max search, alpha-beta reduction, learning description trees, artifical neural networks, perceptions, genetic algorithms, expert systems, natural language process, speech recognition, computer vision.
Course Methodology:
Teaching Methods: 1: Lecture, 2: Question-Answer, 3: Discussion, 4: Lab Work
Course Evaluation Methods:
Assessment Methods: A: Testing, B: Laboratory C: Homework D: Project
Vertical Tabs
Course Learning Outcomes
| Learning Outcomes | Program Learning Outcomes | Teaching Methods | Assessment Methods | |||
| Developing a variety of approaches with general applicability. | 3,6,9 | 1,3,4 | A,B,C | |||
| Acquire a working knowledge of the LISP language, its procedural and data structures | 2,3,6,9 | 1,2,3,4 | A,B,C | |||
| Understand and implement AI search models and generic search strategies | 3,6,9 | 1,3,4 | A,B,C | |||
| Use probability as a mechanism for handling uncertainty in AI. | 2,6,9 | 1,3,4 | A,B,C | |||
| Understand the design of AI systems involving learning to enhance performance. | 3,6,9 | 1,3,4 | A,B,C,D | |||
| Logic and its application as a form of representing knowledge in AI systems | 3,9,6 | 1,2,3,4 | A,B,C,D | |||
| Introducing specific applications such as computer vision, atural language processing, expert systems, | 3,9 | 1,2,3,4 | A,B,C,D | |||
Course Flow
| COURSE CONTENT | ||
| Week | Topics | Study Materials |
| 1 | Introduction, history | ACM 221 |
| 2 | The LISP programming language | ACM 361 |
| 3 | LAB: Program and data structures in LISP. | ACM 369 |
| 4 | Intelligent agents. | ACM 366 |
| 5 | Problem solving, uninformed search | ACM 361,369 |
| 6 | Search and heuristic functions, Local search, Online search, | ACM 111 |
| 7 | MIDTERM EXAMINATION | |
| 8 | Constraint satisfaction | ACM 111 |
| 9 | Game playing, | ACM 369 |
| 10 | Logical agents; propositional logic, Inference in propositional logic, | ACM 363 |
| 11 | First order logic, Inference in first order logic, | ACM 361 |
| 12 | LAB: logic programming, | ACM 361 |
| 13 | Planning problems, | ACM 370 |
| 14 | Expert Systems | ACM 369 |
| 15 | REVIEW AND MIDTERM EXAMINATION | |
Recommended Sources
| RECOMMENDED SOURCES | |
| Textbook | Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach Prentice Hall ISBN-13; 978-0-13-604259-4 (2010) |
| Additional Resources | Peter Norvig, Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp An Imprint of Elsevier. Morgan Kaufmann Publishers San Francisco, CA |
Material Sharing
| MATERIAL SHARING | |
| Documents | Presentations and Laboratory Sheets |
| Assignments | Homework Sheets |
| Exams | Old exam questions are furnished |
Assessment
| ASSESSMENT | ||
| IN-TERM STUDIES | NUMBER | PERCENTAGE |
| Mid-terms | 2 | 66 |
| Quizzes | 4 | 16 |
| Assignment and Labwork | 10 | 18 |
| Total | 100 | |
| CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE | 40 | |
| CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE | 60 | |
| Total | 100 | |
Course’s Contribution to Program
| COURSE'S CONTRIBUTION TO PROGRAM | |||||||
| No | Program Learning Outcomes | Contribution | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Information Systems graduates have the knowledge and the skills to design and develop the complete systems for multi-media visual user interface. (ACM 112,262) | ||||||
| 2 | Information Systems graduates have advanced the knowledge and skills to design, develop and install the application systems for multi-media. (ACM365, 368,473) | x | |||||
| 3 | Information Systems graduates have the knowledge and the skills to design, develop and apply algorithms and data structures to solve the basic problems of information processing, within the framework of discrete mathematics (ACM 221,222). | X | |||||
| 4 | Information Systems graduates have the knowledge and the skills to design and develop computer applications, based on user specificed requirements, using modern structured development tools and install them on various hardware platforms and deploy their usage.(ACM 311,322) | X | |||||
| 5 | Information Systems graduates have the knowledge and the skills to design and develop computer applications, based on user specificed requirements, using modern object-oriented development tools and install them on various hardware platforms and deploy their usage(ACM 321). | X | |||||
| 6 | Information Systems graduates know the logic of computer operating systems, the basic set of system commands, how to control access to system resources by users of different departments and how to monitor the running of jobs in the system (ACM 369, 370). | X | |||||
| 7 | Information Systems graduates have the knowledge and the skills to design and develop data models serving different requirements, database applications that would access and process data using various types of software, including queries, reports and business applications.(ACM 211, 364) | X | |||||
| 8 | Information Systems graduates have the knowledge and the skills to design and develop business applications that would provide data acess, modification and processing for data kept in enterprise database systems (ACM 221,364). | ||||||
| 9 | Information Systems graduates have the knowledge about computer networks, and have the skills to design, develop and monitor computer networks, how to configure them and how to maintain their performance. (ACM 361, 362, 363, 463, 464) | X | |||||
| 10 | Information Systems graduates have the knowledge and the skills to design and develop visual user interfaces for the web, web-based applications for n-tier client/server configurations, how to deploy them in enterprises (ACM 365, 368, 412). | x | |||||
ECTS
| ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION | |||
| Activities | Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 3 | 48 |
| Mid-terms | 2 | 2 | 4 |
| Quizzes | 4 | 1 | 4 |
| Homework | 10 | 3 | 30 |
| Final examination | 2 (Including reparation) | 2 | 4 |
| Total Work Load | 138 | ||
| Total Work Load / 25 (h) | 5.52 | ||
| ECTS Credit of the Course | 6 | ||
None