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Information
| Code | ISB0012 |
| Name | Statistics for Machine Learning |
| Semester | . Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator |
Course Goal
To provide students with knowledge and application skills within the framework of machine learning.
Course Content
Introduction to Machine Laerning, Overview of Regression Models, Penalized Linear Regression, Model Building, Classification, Clustering, Support Vector Machine, Neural Networks, Phyton Applications.
Course Precondition
none
Resources
Dangeti, P. 2017. Statistics for Machine Learning. Packt Publishing, Birmingham
Notes
lecture notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | To be able to explain regression models |
| LO02 | To be able to explain the difference between regression models |
| LO03 | To be able to make statistical analyzes such as classification and clustering |
| LO04 | To interpret neural networks |
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. | 3 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Can use the knowledge gained in the field of statistics in interdisciplinary studies. | 4 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Can organize projects and events in the field of statistics. | 5 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Can perform the stages of creating a project, executing it and reporting the results. | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Have the ability of scientific analysis. | 5 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Can produce scientific publications in the field of statistics. | 4 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Have analytical thinking skills. | 3 |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Can follow professional innovations and developments both at national and international level. | 4 |
| 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. | 5 |
| PLO13 | Bilgi - Kuramsal, Olgusal | Can use information technologies at an advanced level. | 4 |
| PLO14 | Bilgi - Kuramsal, Olgusal | Have the ability to work individually and make independent decisions. | 3 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have the qualities necessary for teamwork. | |
| PLO16 | Bilgi - Kuramsal, Olgusal | Have a sense of professional and ethical responsibility. | 3 |
| PLO17 | Bilgi - Kuramsal, Olgusal | Acts in accordance with scientific ethical rules. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Statistical terminology for model building and validation | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 2 | Machine learning terminology for model building and validation | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 3 | Comparison between regression and machine learning models | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 4 | Machine learning models - ridge and lasso regression | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama, Problem Çözme |
| 5 | Maximum likelihood estimation, Logistic regression – introduction and advantages | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 6 | Tree-based machine learning models | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 7 | Project preparation | Reading the related references | Ölçme Yöntemleri: Performans Değerlendirmesi |
| 8 | Decision tree classifier | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 9 | Bagging classifier, random forest classifier | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 10 | AdabBoost classifier, Gradient boosting classifier | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 11 | K-Nearest Neighbors | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 12 | Naive Bayes | Reading the related references | Öğretim Yöntemleri: Anlatım |
| 13 | Support vector machines and neural networks | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 14 | Unsupervised learning | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 15 | Reinforcement Learning | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 16 | Data analysis | Reading the related references | Öğretim Yöntemleri: Alıştırma ve Uygulama, Proje Temelli Öğrenme |
| 17 | Final examination | Reading the related references | Ölçme Yöntemleri: Performans Değerlendirmesi |
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 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||