School/Faculty/Institute | Faculty of Engineering | ||||
Course Code | DSAI 101 | ||||
Course Title in English | Introduction to DS and AI | ||||
Course Title in Turkish | Veri Bilimi ve Yapay Zekaya Giriş | ||||
Language of Instruction | EN | ||||
Type of Course | Guided Personal Study | ||||
Level of Course | Introductory | ||||
Semester | Fall | ||||
Contact Hours per Week |
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Estimated Student Workload | 156 hours per semester | ||||
Number of Credits | 1 ECTS | ||||
Grading Mode | Pass / Fail | ||||
Pre-requisites | None | ||||
Expected Prior Knowledge | None | ||||
Co-requisites | None | ||||
Registration Restrictions | Only Undergraduate Students | ||||
Overall Educational Objective | To learn the fundamental concepts of Artificial Intelligence and Data Science and to become familiar with basic aspects of intelligent agents, knowledge representation, learning, and sensing. | ||||
Course Description | This course provides a comprehensive introduction to some fundamental aspects of Artificial Intelligence and Data Science. The following topics are covered: Introduction to Data Science, History and Future of Data Science, Big Data Analytics , Data Visualization, Introduction to Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning, Natural Language Processing and some applications of Data Science and Artificial Intelligence. | ||||
Course Description in Turkish | Bu ders, Yapay Zeka ve Veri Biliminin bazı temel yönlerine kapsamlı bir giriş sağlar. Aşağıdaki konular ele alınmaktadır: Veri Bilimine Giriş, Veri Biliminin Tarihi ve Geleceği, Büyük Veri Analitiği, Veri Görselleştirme, Yapay Zekaya Giriş, Makine Öğrenimi, Sinir Ağları, Derin Öğrenme, Doğal Dil İşleme ve Veri Bilimi ve Yapay Zekanın bazı uygulamaları. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) elaborate on the fundamental concepts related to artificial intelligence and data science while communicating effectively with a range of audiences via subject and case presentations; 2) recognize and discuss ethical and professional responsibilities in engineering situations that are directly related to artificial intelligence, data science and related technologies while considering the impact of engineering solutions in global, economic, environmental, and societal contexts; 3) acquire and demonstrate over a case related to contemporary issues in artificial intelligence and data processing/visualization etc. using appropriate learning strategies. |
Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 |
---|---|---|---|
1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | |||
2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | |||
3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | |||
4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | |||
5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | |||
6) Internalization and dissemination of professional ethical standards. | |||
7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | |||
8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | |||
9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | |||
10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | |||
11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | |||
12) Ability to acquire knowledge independently, and to plan one’s own learning. | |||
13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
N None | S Supportive | H Highly Related |
Program Outcomes and Competences | Level | Assessed by | |
1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
6) | Internalization and dissemination of professional ethical standards. | N | |
7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
Prepared by and Date | TUNA ÇAKAR , November 2023 |
Course Coordinator | TUNA ÇAKAR |
Semester | Fall |
Name of Instructor | Asst. Prof. Dr. TUNA ÇAKAR |
Week | Subject |
1) | What is Data Science? |
2) | The Place of Data Science in the Data Universe |
3) | Ethics and Agency |
4) | Sources of Data |
5) | Sources of Rules |
6) | Tools for Data Science |
7) | Mathematics for Data Science |
8) | Unsupervised Learning |
9) | Supervised Learning |
10) | Generative Methods in Data Science, Acting on Data Science |
11) | Artificial Intelligence and Business Strategy I |
12) | Artificial Intelligence and Business Strategy II |
13) | Finding Opportunities, Presenting Your Experience |
14) | Building an Online Presence, Networking and Interviews |
15) | Final Exam/Project/Presentation |
16) | Final Exam/Project/Presentation |
Required/Recommended Readings | Shared videos | ||||||
Teaching Methods | Guided personal study | ||||||
Homework and Projects | Presentation (%40), Final Exam (%60) | ||||||
Laboratory Work | - | ||||||
Computer Use | Required | ||||||
Other Activities | - | ||||||
Assessment Methods |
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Course Administration |
dsai@mef.edu.tr 05309225505 Instructor’s office: 5th floor Phone number: 0 212 395 37 50 Office hours: TBA E-mail address: cakart@mef.edu.tr |
Activity | No/Weeks | Hours | Calculation | ||||
No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
Course Hours | 14 | 1 | 2 | 42 | |||
Laboratory | 10 | 1 | 2 | 30 | |||
Study Hours Out of Class | 1 | 1 | 10 | 11 | |||
Project | 1 | 5 | 25 | 30 | |||
Homework Assignments | 10 | 1 | 2 | 30 | |||
Final Examination | 1 | 10 | 3 | 13 | |||
Total Workload | 156 | ||||||
Total Workload/25 | 6.2 | ||||||
ECTS | 1 |