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| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| STATISTICS (PhD) | |
| Code | ISB601 |
| Name | Statistics for Machine Learning |
| 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 | Prof. Dr. MAHMUDE REVAN ÖZKALE ATICIOĞLU |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
To provide knowledge and application skills within the framework of machine learning.
Course Content
Introduction to Machine Learning, Overview of Regression Models, Penalized Regression Models, Model Building, Classification, Clustering, Support Vector Machines, Neural Networks, Python Applications
Course Precondition
None
Resources
Dangeti, P. 2017. Statistics for Machine Learning. Packt Publishing, Birmingham
Notes
Dangeti, P. 2017. Statistics for Machine Learning. Packt Publishing, Birmingham
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains regression models and interprets the differences between statistical models. |
| LO02 | Performs statistical analyses such as classification and clustering problems. |
| LO03 | Interprets neural networks |
| LO04 | Explains the basic principles and application areas of recommendation systems. |
| LO05 | Applies data preprocessing steps. |
| LO06 | Develops an end-to-end machine learning project on real datasets and interprets the results. |
| LO07 | Distinguishes between classification and regression problems and selects the appropriate algorithm. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Develops new methods and strategies in modeling statistical problems and generating problem-specific solutions. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Can do detailed research on a specific subject in the field of statistics. | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Have a good command of statistical theory to contribute to the statistical literature. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Can use the knowledge gained in the field of statistics in interdisciplinary studies. | 3 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Can organize projects and events in the field of statistics. | 4 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Can perform the stages of creating a project, executing it and reporting the results. | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Have the ability of scientific analysis. | 2 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Can produce scientific publications in the field of statistics. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Have analytical thinking skills. | |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Can follow professional innovations and developments both at national and international level. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Can follow statistical literature. | |
| PLO12 | Beceriler - Bilişsel, Uygulamalı | Can improve his/her foreign language knowledge at the level of making publications and presentations in a foreign language. | |
| PLO13 | Bilgi - Kuramsal, Olgusal | Can use information technologies at an advanced level. | |
| PLO14 | Bilgi - Kuramsal, Olgusal | Have the ability to work individually and make independent decisions. | |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have the qualities necessary for teamwork. | 4 |
| PLO16 | Bilgi - Kuramsal, Olgusal | Have a sense of professional and ethical responsibility. | |
| PLO17 | Bilgi - Kuramsal, Olgusal | Acts in accordance with scientific ethical rules. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Statistics terminology for model building and validation | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 2 | Machine learning terminologies for model building and validation | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 3 | Comparison between regression and machine learning models. | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Machine learning models - lasso, elastic net and their derivatives | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 5 | Maximum likelihood estimation, logistic regression, random forest | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Tree-based machine learning models | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | K-Nearest Neighbors and Naive Bayes | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Midterm exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Support Vector Machines | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Neural Networks | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Recommendation Engines- Content-based filtering, Collaborative filtering, hybrid systems and Evaluation of recommendation engine model | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Unsupervised Learning-K-means clustering, Principal component analysis, Singular value decomposition | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Reinforcement Learning- Dynamic programming, Monte Carlo methods, Temporal difference learning | Reading Sources | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Data analysis applications with Python 1 | Reading Sources | Öğretim Yöntemleri: Örnek Olay, Proje Temelli Öğrenme |
| 15 | Data analysis applications with Python 2 | Reading Sources | Öğretim Yöntemleri: Örnek Olay, Problem Çözme |
| 16 | Term Exams | Final Exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Final Exam | Ölçme Yöntemleri: Yazılı Sınav |
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) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 2 | 20 | 40 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 154 | ||
| Total Workload / 25 (h) | 6,16 | ||
| ECTS | 6 ECTS | ||