ISB0012 Statistics for Machine Learning

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code ISB0012
Name Statistics for Machine Learning
Term 2024-2025 Academic Year
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 / Objective

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

Update Time: 09.05.2024 02:16