TÜRKÇE
ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| COMPUTER ENGINEERING (MASTER) (WITH THESIS) (ENGLISH) | |
| Code | CENG509 |
| Name | Machine Learning |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi BUSE MELİS ÖZYILDIRIM |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
To be able to comprehend, apply and analyze machine learning algorithms
Course Content
It includes theoretical analysis of machine learning algorithms, implementation on sample data sets and analysis of results
Course Precondition
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Comprehends the principles of machine learning algorithms |
| LO02 | Learns to choose the appropriate algorithm for the problem |
| LO03 | Implements the algorithms on different datasets |
| LO04 | Learns to make hyperparameter settings |
| LO05 | Learns to analyze the results |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | 5 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 5 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 5 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 5 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | 5 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | 5 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | 5 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | 5 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | 5 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. | 5 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to machine learning algorithms | Research on the subject | |
| 2 | Examination of the methods used in the performance measurement of algorithms | Research on the subject | |
| 3 | Describing linear regression, logistic regression | Research on the subject | |
| 4 | Perceptron, Multi layer perceptron, gradient descent method, backpropagation learning | Research on the subject | |
| 5 | Probabilistic approaches, Bayes classification, Naive Bayes method | Research on the subject | |
| 6 | Unsupervised learning methods (clusterig and mapping methods) | Research on the subject | |
| 7 | Dimension reduction methods | Research on the subject | |
| 8 | Mid-Term Exam | Reading the notes | |
| 9 | Decision Tree | Research on the subject | |
| 10 | Ensemble learning methods (Random forests, Adaboost) | Research on the subject | |
| 11 | Fuzzy logic method | Research on the subject | |
| 12 | Support vector machine | Research on the subject | |
| 13 | Fuzzy logic method | Research on the subject | |
| 14 | Support vector machine | Research on the subject | |
| 15 | Reinforcement learning - Q learning | Research on the subject | |
| 16 | Term Exams | Reading the notes | |
| 17 | Term Exams | Reading notes |