Computer Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05081910 | Advanced Programming | 4 | Spring | 8 | 3+0+0 | 3 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Asst. Prof. Ali Osman ÇIBIKDİKEN |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Ali Osman ÇIBIKDİKEN | A-124 | [email protected] | 7585 | Monday 14.00-15.00 |
Course Content
History of Programming Languages, Syntax and Meaning, Control Structures, Data Types, Data Flow, Logic Programming, Functional Programming and Lambda Calculation, Simultaneous and Distributed Programming, Agent-Based Programming, Subject-Based Programming, View-Based Programming, Service-Based Programming.
Objectives of the Course
This course will introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization and text analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, opencv, keras, tensorflow, yolo to gain insight into their data.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | X |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P2 | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose | 5 |
| P3 | Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Students gain detailed knowledge of the concepts, costs and limitations of high-level programming languages | P.2.27 | 1 |
| O2 | Students know high-level abstraction techniques of programming. | P.2.28 | 1,7 |
| O3 | Students become familiar with advanced software development principles, techniques and best practices. | P.3.28 | 1,7 |
| O4 | Students have knowledge about programming language fields and their purposes. | P.3.29 | 1 |
| O5 | Students know programming languages classes. | P.2.29 | 1,7 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction and familiarization with the teaching environment. |
| 2 | Introduction to python: the first python program, basics of python syntax, data types of python, basic operations of python, functions, modules and packages of python, conditions, range, loops. |
| 3 | Introduction to python: break, continue and else in loops, self-defined functions, recursions, scope of variable: standart library functions, exceptions. |
| 4 | Data acqusition and presentation |
| 5 | Powerful data structures and python extension libraries: dictionary use, extension library SciPy, ndarray, dataframe |
| 6 | Python data statistics and visualization: data preperations, data display, data selection, simple statistics and processing, grouping, merge, cluster, basics of matplotlib plotting. |
| 7 | Project 1 Presentation |
| 8 | Midterm Exam |
| 9 | Applied machine learning in python |
| 10 | Introduction to keras in python |
| 11 | Digit classification using deep learning and conventional methods |
| 12 | Introduction to Darknet YOLO in python |
| 13 | Object Detection and recognition using YOLO |
| 14 | Project 2 Presentation |
| 15 | Final Exam |
Textbook or Material
| Resources | Robert W. Sebesta: ''Concepts of Programming Languages'', 9th ed., Addison Wesley 2009 |
| Robert W. Sebesta: ''Concepts of Programming Languages'', 9th ed., Addison Wesley 2009 |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 4 | 56 |
| Midterms | 1 | 3 | 3 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 3 | 3 |
| Other | 14 | 4 | 56 |
| Total Work Load: | 160 | ||
| Total Work Load / 30 | 5,33 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P2 | P3 |
|---|---|---|---|
| O1 | Students gain detailed knowledge of the concepts, costs and limitations of high-level programming languages | 4 | - |
| O2 | Students know high-level abstraction techniques of programming. | - | 2 |
| O3 | Students know programming languages classes. | 5 | - |
| O4 | Students become familiar with advanced software development principles, techniques and best practices. | - | 1 |
| O5 | Students have knowledge about programming language fields and their purposes. | 1 | 2 |