BUSINESS ADMINISTRATION (TURKISH) | |||||
Associate | TR-NQF-HE: Level 5 | QF-EHEA: Short Cycle | EQF-LLL: Level 5 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
CMP4501 | Introduction to Artificial Intelligence and Expert Systems | Spring Fall |
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: | Associate (Short 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) | He/she applies theoretical business management knowledge on business life. | |
2) | He/she uses business management function and applies and follows new management technics. | |
3) | He/she performs legal responsibilities of business, follows and applies legislation. | |
4) | He/she can use information and communication tecnologies that necessary for their area, follows technological change and applies new technologies to business system. | |
5) | He/she determines, analyses and solves problems that appear in vocational applications. | |
6) | He/she manages business finance, analyses business financial situation, and solves financial problems. | |
7) | He/she gains management skill by managing and enhanging human resource effectively in accordance with business aims. | |
8) | He/she can understand production and marketing functions as a whole, applies new production and marketing techniques. | |
9) | He/she calculates cost, makes entry, prepares and interprets financial statements. | |
10) | He/she has vocational ethical value that market needs in qualified staff, can use Turkish effectively in writing and speaking communication; have proficiency in foreign language knowledge to making international correspond. | |
11) | He/she follows and applies vocational current and economic developments in national and international area. | 2 |