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Information
| Code | YZ002 |
| Name | Statistical Learning for Data Science and Artificial Intelligence |
| Term | 2024-2025 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 | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
Prof. Dr. MAHMUDE REVAN ÖZKALE
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
Application of artificial intelligence, data science and machine learning concepts to real life problems
Course Content
Application of artificial intelligence, data science and machine learning concepts to real life problems
Course Precondition
There is no prerequisite for the course.
Resources
G. James, D. Witten, T. Hastie, R. Tibshirani, J. Taylor. An Introduction to Statistical Learning: with Applications in Python, Springer, 2023, 1431-875X 978-1-46147-7138-7(eBook)
Notes
M. Bowles, Machine Learning in Python: Essential Techniques for Predictive Analysis, Wiley, 2015
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Applies classification methods |
| LO02 | Does multiple regression analysis |
| LO03 | Analyzes with model selection methods |
| LO04 | Applies resampling methods |
| LO05 | Applies logistic regression analysis |
| LO06 | Creates model in high dimensional data |
| LO07 | Applies support vector machine |
| LO08 | Analysis data via Python |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Beceriler - Bilişsel, Uygulamalı | To be able to access information broadly and deeply by conducting scientific research in the field, to be able to evaluate, interpret and apply the information. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Has a comprehensive knowledge of current techniques and methods applied in engineering and their limitations. | 4 |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | To be able to use uncertain, limited or incomplete data to complete and apply knowledge using scientific methods; to be able to use knowledge from different disciplines together. | 5 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Is aware of new and emerging practices of the profession, examines and learns them when needed. | 4 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Defines and formulates problems related to the field, develops methods to solve them and applies innovative methods in solutions. | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Develops new and/or original ideas and methods; designs complex systems or processes and develops innovative/alternative solutions in their designs. | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Designs and implements theoretical, experimental and modeling-based research; examines and solves complex problems encountered in this process. | 5 |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | To be able to work effectively in disciplinary and multidisciplinary teams, to lead such teams and to develop solution approaches in complex situations; to be able to work independently and take responsibility. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | To be able to communicate orally and in writing in a foreign language at least at the B2 level of the European Language Portfolio. | |
| PLO10 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | To be able to communicate the process and results of his/her studies systematically and clearly in written or oral form in national and international environments in or outside the field. | |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Knows the social, environmental, health, safety, legal, project management and business life practices of engineering applications and is aware of the constraints these impose on engineering applications. | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Statistical Learning | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 2 | Multiple Linear Regression | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 3 | Model Inadequacies in Multiple Linear Regression | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 4 | Logistic Regression | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 5 | Regression Analysis with Python | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 6 | Classification | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 7 | Classification Analysis with Python | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Resampling Methods (Cross Validation and Bootstrap) | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 10 | Model Selection Methods (Submodel Selection and Regularization) | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 11 | Model Building in High Dimensional Data | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 12 | Tree-Based Methods | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 13 | Tree-Based Methods with Python | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 14 | Support Vector Machines | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 15 | Unsupervised Learning (Principal Component Analysis and Clustering) | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Preparation for the 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 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 15 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 20 | 20 |
| Total Workload (Hour) | 162 | ||
| Total Workload / 25 (h) | 6,48 | ||
| ECTS | 6 ECTS | ||