Information Package / Course Catalogue
| Big Data Analytics (English) (Non-Thesis) | |||||
| Master | Length of the Programme: 1.5 | Number of Credits: 90 | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF: Level 7 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Gradutate School of Science and Engineering | |||||
| Course Code | BDA 502 | |||||
| Course Title in English | Introduction to Machine Learning | |||||
| Course Title in Turkish | Yapay Öğrenmeye Giriş | |||||
| Language of Instruction | EN | |||||
| Type of Course | Exercise,Flipped Classroom,Lecture | |||||
| Level of Course | Intermediate | |||||
| Semester | Spring | |||||
| Contact Hours per Week |
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| Estimated Student Workload | 157 hours per semester | |||||
| Number of Credits | 7.5 ECTS | |||||
| Grading Mode | Standard Letter Grade | |||||
| Pre-requisites | None | |||||
| Expected Prior Knowledge | Basic probability knowledge Basic programming (python) knowledge | |||||
| Co-requisites | None | |||||
| Registration Restrictions | Only Graduate Students | |||||
| Overall Educational Objective | To learn the basic data analytics process with on hands applications using modern tools to explore data by summarizing, slicing/dicing and analyzing data via graphical and quantitative tools. | |||||
| Course Description | This course will provide insight into the basics of using machine learning algorithms to quantify operational implications of the Big Data Analytics. The course content will introduce the main principles and methods of machine learning including Naïve Bayes, Support Vector Machines (SVM), Decision Trees, Neural Networks and others. This course aims to provide the theoretical and practical dimensions for the machine learning algorithms applied to real-world problems especially related to Big Data. | |||||
| Course Description in Turkish | Bu ders makine öğrenme algoritmaları kullanılarak Büyük Veri Analitiğinin operasyonel çıkarımlarının ölçülebilir hale getirilmesi konusunda bir çerveve sağlayacaktır. Dersin içeriği makine öğrenmede kullanılan temel ilke ve metotların (Naive Bayes, Support Vector Machines, Decision Trees vbg.) öğrencilere tanıtılmasını kapsamaktadır. Bu ders özellikle Büyük Veri ile ilgili gerçek dünya sorunlarına makine öğrenme algoritmalarını kullanarak teorik ve pratik boyut sağlamayı hedeflemektedir. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) To discuss on the basic techniques and issues of the machine learning field 2) To understand the most common dimensionality reduction algorithms 3) To implement Principal Component Analysis (PCA) and Fisher Linear Discriminant 4) To understand the most common classification and clustering algorithms 5) To implement the Support Vector Machine (SVM) and other classification algorithms 6) To decide on which ML algorithm to apply given a problem |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| 1) Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. | ||||||
| 2) Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | ||||||
| 3) Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | ||||||
| 4) Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. | ||||||
| 5) Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. | ||||||
| 6) Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. | ||||||
| 7) Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. | ||||||
| 8) Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. | ||||||
| 9) Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. | ||||||
| 10) Understanding of social and environmental aspects of machine learning applications. |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. | H | |
| 2) | Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | H | |
| 3) | Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | H | |
| 4) | Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. | S | |
| 5) | Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. | S | |
| 6) | Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. | H | |
| 7) | Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. | S | |
| 8) | Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. | N | |
| 9) | Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. | S | |
| 10) | Understanding of social and environmental aspects of machine learning applications. | N |
| Prepared by and Date | , |
| Course Coordinator | TUNA ÇAKAR |
| Semester | Spring |
| Name of Instructor | Asst. Prof. Dr. TUNA ÇAKAR |
Course Contents
| Week | Subject |
| 1) | Introduction to Machine Learning |
| 2) | Introduction to Machine Learning |
| 3) | Naïve Bayes |
| 4) | Naïve Bayes |
| 5) | Support Vector Machines (SVM) |
| 6) | Support Vector Machines (SVM) |
| 7) | Decision Trees |
| 8) | Decision Trees |
| 9) | Regression Models |
| 10) | Outliers |
| 11) | Clustering |
| 12) | Clustering |
| 13) | Feature Selection (PCA) |
| 14) | Validation & Evaluation Metrics |
| 15) | Final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | None | |||||||||||||||
| Teaching Methods | Flipped classroom/Exercise/Laboratory/Active learning | |||||||||||||||
| Homework and Projects | Students are required to complete a portfolio to be able to enter the final exam | |||||||||||||||
| Laboratory Work | Students will apply the methods they learned using the laboratory hours | |||||||||||||||
| Computer Use | Students will apply the methods they learned using the laboratory hours | |||||||||||||||
| Other Activities | None | |||||||||||||||
| Assessment Methods |
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| Course Administration |
cakart@mef.edu.tr 02123953600 |
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ECTS Student Workload Estimation
| 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 | 2 | 1.5 | 2 | 77 | ||
| Project | 9 | 2 | 4 | 54 | |||
| Final Examination | 1 | 30 | 3 | 33 | |||
| Total Workload | 164 | ||||||
| Total Workload/25 | 6.6 | ||||||
| ECTS | 7.5 | ||||||