MOLECULAR BIOLOGY AND GENETICS | |||||
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) | Utilize the wealth of information stored in computer databases to answer basic biological questions and solve problems such as diagnosis and treatment of diseases. | 3 |
2) | Acquire an ability to compile and analyze biological information, clearly present and discuss the conclusions, the inferred knowledge and the arguments behind them both in oral and written format. | 4 |
3) | Develop critical, creative and analytical thinking skills. | 5 |
4) | Develop effective communication skills and have competence in scientific speaking, reading and writing abilities in English and Turkish. | 3 |
5) | Gain knowledge of different techniques and methods used in genetics and acquire the relevant laboratory skills. | 4 |
6) | Detect biological problems, learn to make hypothesis and solve the hypothesis by using variety of experimental and observational methods. | 4 |
7) | Gain knowledge of methods for collecting quantitative and qualitative data and obtain the related skills. | 3 |
8) | Conduct research through paying attention to ethics, human values and rights. Pay special attention to confidentiality of information while working with human subjects. | 5 |
9) | Obtain basic concepts used in theory and practices of molecular biology and genetics and establish associations between them. | 4 |
10) | Search and use literature to improve himself/herself and follow recent developments in science and technology. | 5 |
11) | Be aware of the national and international problems in the field and search for solutions. | 4 |