AMERICAN CULTURE AND LITERATURE | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
CMP4501 | Introduction to Artificial Intelligence and Expert Systems | Spring | 3 | 0 | 3 | 6 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
Type of course: | Non-Departmental Elective |
Course Level: | Bachelor’s Degree (First Cycle) |
Mode of Delivery: | Face to face |
Course Coordinator : | Dr. Öğr. Üyesi TEVFİK AYTEKİN |
Recommended Optional Program Components: | None |
Course Objectives: | The course introduces basics of artificial intelligence. Basic search techniques used for problem solving, fundamentals of knowledge representation and logical formalisms, basic learning algorithms, and fundamentals of expert systems will be introduced. |
The students who have succeeded in this course; I. Be able to formulate a state space description of a problem II. Be able to select and implement brute-force or heuristic algorithm for a problem. III. Be able to implement minimax search with alpha-beta pruning. IV. Be able to compare and evaluate the most common models for knowledge representation. V. Be able to explain the operation of the resolution technique for theorem proving. VI.Be able to explain the differences among supervised and unsupervised learning. VII. Be able to explain the concepts of overfitting, underfitting, bias, and variance. VIII. Be able to implement some of the basic algorithms for supervised learning and unsupervised learning. IX. Be able to describe fundamentals of expert systems and evaluate them. |
Introduction to AI, state spaces and searching, heuristic functions and search, alpha-beta pruning, propositional and first-order predicate logic, propositional and first order inference, unification and resolution, linear regression, logistic regression, neural networks and backpropagation algorithm, Bayes’ rule and naive Bayes algorithm, clustering and k-means algorithm, fundementals of expert systems, software for expert systems. |
Week | Subject | Related Preparation |
1) | Introduction to AI | |
2) | State spaces and searching. | |
3) | Heuristic functions and search | |
4) | Decisions in games, alpha-beta pruning. | |
5) | Propositional and first-order predicate logic | |
6) | Propositional and first order inference | |
7) | Unification and resolution | |
8) | Linear Regression | |
9) | Midterm | |
10) | Logistic Regression | |
11) | Neural networks and backpropagation algorithm. | |
12) | Bayes’s rule and naive Bayes algorithm. | |
13) | Clustering and k-means algorithm | |
14) | Fundementals of expert systems. | |
15) | Software for expert systems. |
Course Notes / Textbooks: | Russell, S., Norvig, P., Artificial Intelligence: A Modern Approach, (3rd edition), 2009. Giarratano, J.C., Riley, G.D., Expert Systems: Principles and Programming, (4th edition), 2004. |
References: | Yok - None |
Semester Requirements | Number of Activities | Level of Contribution |
Quizzes | 2 | % 10 |
Project | 1 | % 20 |
Midterms | 1 | % 30 |
Final | 1 | % 40 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 40 | |
PERCENTAGE OF FINAL WORK | % 60 | |
Total | % 100 |
Activities | Number of Activities | Workload |
Course Hours | 14 | 42 |
Project | 4 | 20 |
Homework Assignments | 10 | 20 |
Quizzes | 2 | 8 |
Midterms | 5 | 15 |
Final | 5 | 20 |
Total Workload | 125 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Upon graduation, students will acquire key skills and attributes to conduct research to use research tools, to solve problems, to communicate effectively and to transfer skills to the workplace. | |
2) | Upon graduation, students will have developed the ability to discuss key issues in fluent English. | |
3) | Upon graduation, students will have developed the ability to compose written documents in English with a mature prose style. | 4 |
4) | Upon graduation, students will have gained broad knowledge of the American and English literary canons. | 4 |
5) | Upon graduation, students will have developed the ability to analyze, synthesize and criticize sophisticated works of American and English literature. | 4 |
6) | Upon graduation, students will have achieved in depth the understanding of contemporary American culture. | 3 |
7) | Upon graduation, students will have developed the ability to draw links among diverse literary texts and documents and establish critical connections and adopt an interdisciplinary attitude. | 3 |
8) | Upon graduation, students will be able to develop new projects individually or in teams. | 3 |
9) | Upon graduation, students will be able to apply their knowledge into their lives for interdisciplinary problem-solving and solutions. | 4 |