| School/Faculty/Institute | Faculty of Engineering | ||||
| Course Code | DSAI 201 | ||||
| Course Title in English | Fundamentals of Machine Learning and Data Science | ||||
| Course Title in Turkish | Makine Öğrenmesinin Temelleri ve Veri Bilimi | ||||
| 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 fundamental vocabulary and concepts of machine learning and take a deeper dive into the details of several of the most important machine learning approaches, and develop intuition into how they work and when and where they are applicable. | ||||
| Course Description | This course provides a comprehensive introduction to the field of machine learning. Firstly, the practical aspects of machine learning using Python's Scikit-Learn package will be examined. Contains the following topics: Supervised Learning, Unsupervised Learning, Linear Classifiers, Tree-Based Models and Data Science. | ||||
| Course Description in Turkish | Bu kurs, makine öğrenimi alanına kapsamlı bir giriş niteliğindedir. Öncelikle Python'un Scikit-Learn paketini kullanarak makine öğreniminin pratik yönleri incelenecektir. Aşağıdaki konuları içerir:Denetimli Öğrenme, Denetimsiz Öğrenme, Doğrusal Sınıflandırıcılar, Ağaç Tabanlı Modeller ve Veri Bilimi. |
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 machine learning; 2) an ability to use the Scikit-Learn API and show some examples of its use; 3) an ability to take a deeper dive into the details of several of the most important machine learning approaches, and develop intuition into how they work and when and where they are applicable. |
| 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. ŞİRİN ÖZLEM |
| Week | Subject |
| 1) | Supervised Learning with scikit-learn |
| 2) | Supervised Learning with scikit-learn |
| 3) | Supervised Learning with scikit-learn |
| 4) | Unsupervised Learning in Python |
| 5) | Unsupervised Learning in Python |
| 6) | Linear Classifiers in Python |
| 6) | Linear Classifiers in Python |
| 7) | Linear Classifiers in Python |
| 8) | Linear Classifiers in Python |
| 9) | Tree-Based Models in Python |
| 10) | Tree-Based Models in Python |
| 11) | Tree-Based Models in Python |
| 12) | Introduction to Data Science in Python |
| 13) | Introduction to Data Science in Python |
| 14) | Introduction to Data Science in Python |
| 15) | Final Exam/Project/Presentation |
| 16) | Final Exam/Project/Presentation |
| Required/Recommended Readings | - | ||||||
| Teaching Methods | Guided personal study | ||||||
| Homework and Projects | DataCamp Tasks and Quizzes (%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 |
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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 | ||||||