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 522 | ||||
| Course Title in English | Advanced Machine Learning | ||||
| Course Title in Turkish | Yapay Öğrenme II | ||||
| 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 | 174 hours per semester | ||||
| Number of Credits | 7.5 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | Basic machine learning knowledge (BDA 502) | ||||
| Co-requisites | None | ||||
| Registration Restrictions | Only Graduate Students | ||||
| Overall Educational Objective | To learn the machine learning concepts with on hands applications using modern tools to process data via tensorflow to drive conclusions using data analysis tools. | ||||
| Course Description | The aim of the course is to give the fundamentals of machine learning. This course introduces the fundamental framework in machine learning with the use of tensorflow. | ||||
| Course Description in Turkish | Bu dersin amacı makine öğrenmesi temellerini sağlamaktır. Bu derste tensorflow kullanımıyla makine öğrenmedeki temel çerçeve sunulacaktır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Recognize and discuss the contributions of machine learning at an advanced level 2) Understand and apply the philosophy behind machine learning; 3) Develop first at an idea level then as a practical manner a machine learning system 4) Understand the mechanism of a given ML system and further develop it in terms of performance. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 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. | H | |
| 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. | S | |
| 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, Framing, Descending into ML |
| 2) | Reducing Loss |
| 3) | First steps with TensorFlow |
| 4) | Generalization, Training & Test Sets, Validation Set |
| 5) | Representation |
| 6) | Feature Crosses |
| 7) | Regularization: Simplicity & Logistic Regression |
| 8) | Classification |
| 9) | Regularization: Sparsity & Neural Networks |
| 10) | Training Neural Nets & Multi-class Neural Nets |
| 11) | Embeddings |
| 12) | ML Engineering |
| 13) | ML Systems in the Real World |
| 14) | General Review |
| 15) | Final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | https://developers.google.com/machine-learning/crash-course/first-steps-with-tensorflow/toolkit | ||||||||||||||||||
| Teaching Methods | Flipped classroom/Exercise/Laboratory/Active learning | ||||||||||||||||||
| Homework and Projects | Homewrodk and Projects | ||||||||||||||||||
| Laboratory Work | Each week there will be a lab session | ||||||||||||||||||
| Computer Use | Students will apply the methods they learned | ||||||||||||||||||
| 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 | 49 | |||
| Laboratory | 14 | 2 | 1.5 | 49 | |||
| Project | 9 | 2 | 1 | 27 | |||
| Midterm(s) | 1 | 30 | 30 | ||||
| Final Examination | 1 | 30 | 3 | 33 | |||
| Total Workload | 188 | ||||||
| Total Workload/25 | 7.5 | ||||||
| ECTS | 7.5 | ||||||