| 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 | Guided Personal Study | ||||
| 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 101 - Introduction to DS and AI DSAI 102 - Mathematical Foundations for DS and AI DSAI 103 - Programming for DS and AI |
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| Expected Prior Knowledge | None | ||||
| Co-requisites | 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 Description in Turkish | Bu kurs, yapay zeka temelleri ve yapay zeka temel kavramlarına kapsamlı bir giriş sağlar. Aşağıdaki konular ele alınmaktadır: Azure, AI ve ML Temel Kavramları ile yapay zeka Temellerine Giriş, Makine Öğrenimi, Bilgisayarla Görme, Doğal Dil İşleme, Konuşmaya Dayalı Yapay Zeka. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) an ability to introduce the fundamental vocabulary and concepts of artificial intelligence; 2) an ability to train and evaluate models using Azure Machine Learning, using its Automated Machine Learning capabilities; 3) to have a broad knowledge of analyzing text and speech for intent, and translating text and speech between languages. 4) an ability to 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 |
| 16) | Final Exam/Project/Presentation |
| Required/Recommended Readings | - | ||||||
| Teaching Methods | Guided personal study | ||||||
| Homework and Projects | Udacity Quizzes (%10) Final Exam (%50) Video Tasks (%40) | ||||||
| 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 |
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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 | ||||||