| School/Faculty/Institute | Faculty of Engineering | ||||
| Course Code | DSAI 202 | ||||
| Course Title in English | Fundamentals of AI and Robotics | ||||
| Course Title in Turkish | Yapay Zeka ve Robotiğin Temelleri | ||||
| Language of Instruction | EN | ||||
| Type of Course | |||||
| Level of Course | Intermediate | ||||
| Semester | Fall | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 156 hours per semester | ||||
| Number of Credits | 6 ECTS | ||||
| Grading Mode | Pass / Fail | ||||
| Pre-requisites |
DSAI 103 - Programming for DS and AI DSAI 102 - Mathematical Foundations for DS and AI DSAI 101 - Introduction to DS and AI |
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| Co-requisites | None | ||||
| Expected Prior Knowledge | None | ||||
| Registration Restrictions | Only undergraduate students | ||||
| Overall Educational Objective | To learn the basic artificial intelligence concepts and to become familiar with the core concepts of artificial intelligence , to improve training and evaluating model skills and get enough information to apply these. | ||||
| Course Description | This course provides a comprehensive introduction to AI fundamentals and AI core concepts. The following topics are covered: Introduction to AI Fundamentals with Azure, AI and ML Core Concepts, Machine Learning, Computer Vision, Natural Language Processing, Conversational AI. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Understand the fundamental vocabulary and concepts of artificial intelligence; 2) Train and evaluate models using Azure Machine Learning, using its Automated Machine Learning capabilities; 3) Have a broad knowledge of analyzing text and speech for intent, and translating text and speech between languages; 4) Use computer vision for object detection, image classification, face detection, text analysis, and processing forms. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 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 | Prof. Dr. İLKER BEKMEZCİ |
| Week | Subject |
| 1) | Introduction to AI Fundamentals with Azure |
| 2) | AI and ML Core Concepts |
| 3) | AI and ML Core Concepts II |
| 4) | Machine Learning |
| 5) | Machine Learning II |
| 6) | Computer Vision |
| 7) | Computer Vision II |
| 8) | Computer Vision III |
| 9) | Natural Language Processing |
| 10) | Natural Language Processing II |
| 11) | Natural Language Processing III |
| 12) | Conversational AI |
| 13) | Conversational AI II |
| 14) | Conversational AI III |
| 15) | Final Exam/Project/Presentation Period |
| 16) | Final Exam/Project/Presentation Period |
| Required/Recommended Readings | - | ||||||
| Teaching Methods | Guided personal study | ||||||
| Homework and Projects | Udacity Quizzes (%10) Final Exam (%50) Video Tasks (%40) | ||||||
| Laboratory Work | None | ||||||
| Computer Use | Required | ||||||
| Other Activities | None | ||||||
| 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 |
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| 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 | 6 | ||||||