TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF ENGINEERING |
| ELECTRICAL-ELECTRONIC ENGINEERING PR. (ENGLISH) | |
| Code | EEES406 |
| Name | Data Analytics for Internet of Things |
| Term | 2018-2019 Academic Year |
| Semester | 8. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 4 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Belirsiz |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi ERCAN AVŞAR |
| Course Instructor |
Dr. Öğr. Üyesi ERCAN AVŞAR
(Bahar)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
Introduction of basic machine learning methods, gaining theoretical and practical knowledge required for utilization of smart decision methods in IoT applications
Course Content
Importance of data analytics for IoT, Fundamentals of probability and random variables, What is machine learning, Classification and regression problems, Supervised learning: Curve fitting, decision trees, k-nearest neighbor algorithm, Unsupervised learning: clustering methods, principal component analysis Implementation of supervised and unsupervised learning methods in Python, Reading analog and digital data from sensors. Sensor calibration, Realization of smart decision methods using a single board computer Big data analytics: Necessity and requirements
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | - | Has capability in those fields of mathematics and physics that form the foundations of engineering. | |
| PLO02 | - | Grasps the main knowledge in the basic topics of electrical and electronic engineering. | |
| PLO03 | - | Comprehends the functional integrity of the knowledge gathered in the fields of basic engineering and electrical-electronics engineering. | |
| PLO04 | - | Identifies problems and analyzes the identified problems based on the gathered professional knowledge. | |
| PLO05 | - | Formulates and solves a given theoretical problem using the knowledge of basic engineering. | |
| PLO06 | - | Has aptitude for computer and information technologies | |
| PLO07 | - | Knows English at a level adequate to comprehend the main points of a scientific text, either general or about his profession, written in English. | |
| PLO08 | - | Has the ability to apply the knowledge of electrical-electronic engineering to profession-specific tools and devices. | |
| PLO09 | - | Has the ability to write a computer code towards a specific purpose using a familiar programming language. | |
| PLO10 | - | Has the ability to work either through a purpose oriented program or in union within a group where responsibilities are shared. | |
| PLO11 | - | Has the aptitude to identify proper sources of information, reaches them and uses them efficiently. | |
| PLO12 | - | Becomes able to communicate with other people with a proper style and uses an appropriate language. | |
| PLO13 | - | Internalizes the ethical values prescribed by his profession in particular and by the professional life in general. | |
| PLO14 | - | Has consciousness about the scientific, social, historical, economical and political facts of the society, world and age lived in. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Importance of data analytics for IoT | None | |
| 2 | Fundamentals of probability and random variables | Review the previous lecture contents | |
| 3 | What is machine learning. Classification and regression problems | Review the previous lecture contents | |
| 4 | Supervised learning: Curve fitting, decision trees, k-nearest neighbor algorithm | Review the previous lecture contents | |
| 5 | Implementation of supervised learning methods in Python | Review the previous lecture contents | |
| 6 | Unsupervised learning: clustering methods, principal component analysis | Review the previous lecture contents | |
| 7 | Implementation of unsupervised learning methods in Python | Review the previous lecture contents | |
| 8 | Mid-Term Exam | Review the previous lecture contents | |
| 9 | Reading analog and digital data from sensors. Sensor calibration | Review the previous lecture contents | |
| 10 | Realization of smart decision methods using a single board computer | Review the previous lecture contents | |
| 11 | Big data analytics: Necessity and requirements | Review the previous lecture contents | |
| 12 | In-class sample project development | Review the previous lecture contents | |
| 13 | In-class sample project development | Review the previous lecture contents | |
| 14 | Student project studies | Review the previous lecture contents | |
| 15 | Student project studies | Review the previous lecture contents | |
| 16 | Term Exams | Review the previous lecture contents | |
| 17 | Term Exams | Review the previous lecture contents |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |