TÜRKÇE
ENGLISH
Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| ELECTRICAL-ELECTRONICS ENGINEERING (PhD) (ENGLISH) | |
| Code | EE726 |
| Name | İstatistiksel Makine Öğrenimi |
| Term | 2026-2027 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. FATİH KILIÇ |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The objective of this course is to develop a deep understanding of statistical machine learning and to provide students with comprehensive knowledge and skills in this field. The course is designed to provide students with the statistical foundations of modern machine learning methods, to develop their ability to analyze complex data sets and to conduct original research using statistical modeling techniques.
Course Content
This course focuses on foundational statistical principles, providing students with the statistical fundamentals of machine learning methods. The course aims to equip students with the skills to understand, apply, and evaluate machine learning algorithms on diverse datasets. Additionally, it covers advanced topics offering students a comprehensive knowledge base in the field of statistical machine learning.
Course Precondition
There is no prerequisite for the course.
Resources
1. Machine Learning - A Probabilistic Perspective, Kevin P. Murphy, MIT Press, 2012. 2. Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python by Sebastian Raschka, Yuxi (Hayden) Liu , Vahid Mirjalili, 2022. 3. Introduction to Statistical Learning, Python Edition, By Bala Priya C, KDnuggets on July 28, 2023 in Python
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains, analyzes, and applies the statistical foundations of machine learning |
| LO02 | Develops models using supervised and unsupervised learning methods, evaluates their performance using appropriate metrics, and compares different approaches. |
| LO03 | Designs and optimizes models for complex problems using deep learning and modern statistical machine learning approaches |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Being able to specialize in at least one of the branches that form the foundations of Electrical and Electronics Engineering by increasing the level of knowledge beyond the master's level | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | To comprehend the integrity of all the subjects included in the field of specialization. | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Having knowledge of the current scientific literature in the field of specialization to analyze the literature critically | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | To comprehend the interdisciplinary interaction of the field with other related branches, to suggest similar interactions. | 4 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to do theoretical and experimental work | 2 |
| PLO06 | Bilgi - Kuramsal, Olgusal | To create a complete scientific text by compiling the information obtained from the research | 3 |
| PLO07 | Bilgi - Kuramsal, Olgusal | To work on the thesis topic programmatically, following the logical integrity required by the subject within the framework determined by the advisor. | 4 |
| PLO08 | Bilgi - Kuramsal, Olgusal | To search for literature in scientific databases, particularly the ability to correctly and accurately scan databases and evaluate and categorize listed items. | 3 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Having a command of English and related English jargon at a level that can easily read and understand a scientific text written in English in the field of specialization and write a similar text | 3 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Ability to write a computer program in a familiar programming language, generally for a specific purpose, specifically related to the field of expertise. | 3 |
| PLO11 | Bilgi - Kuramsal, Olgusal | Ability to plan and teach lessons related to the field of specialization or related fields | 2 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Being able to guide and take the initiative in environments that require solving problems related to the field | 2 |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Ability to communicate with people in an appropriate language | 3 |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | Adopting the ethical values required by both education and research aspects of academician | 3 |
| PLO15 | Yetkinlikler - Öğrenme Yetkinliği | To be able to produce projects, policies, and processes in the field of expertise and to evaluate these elements | 3 |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | Ability to research new topics based on existing research experience | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Overview of machine learning and its statistical foundations, basic concepts in probability theory and statistical inference | ||
| 2 | Linear regression and multiple linear regression, model evaluation and performance metrics | ||
| 3 | Regularization techniques (L1 and L2 regularization) | ||
| 4 | Polynomial regression and Logistic regression | ||
| 5 | Decision trees and ensemble methods (Random Forests, Gradient Boosting) | ||
| 6 | Support Vector Machines (SVM): Classification | ||
| 7 | Support Vector Machines (SVM): Regression | ||
| 8 | Mid-Term Exam | ||
| 9 | K-means clustering, hierarchical clustering methods, evaluation of clustering algorithms | ||
| 10 | Dimensionality Reduction, principal component analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE) | ||
| 11 | Bayesian methods in machine learning | ||
| 12 | Deep Learning: Neural network architecture and activation functions | ||
| 13 | Training neural networks: backpropagation and optimization techniques | ||
| 14 | Recent advancements and trends in statistical machine learning | ||
| 15 | Bayesian analysis | ||
| 16 | Term Exams | ||
| 17 | Term Exams |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 16 | 5 | 80 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 24 | 24 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 2 | 2 |
| Final Exam | 1 | 2 | 2 |
| Total Workload (Hour) | 150 | ||
| Total Workload / 25 (h) | 6,00 | ||
| ECTS | 6 ECTS | ||