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Information
| Code | ISB420 |
| Name | Machine Learning with Python |
| Term | 2023-2024 Academic Year |
| Semester | 8. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MAHMUDE REVAN ÖZKALE |
| Course Instructor |
1 |
Course Goal / Objective
Application of artificial intelligence, data science and machine learning concepts to real life problems
Course Content
Ability to analyze and interpret original data using Python programming language
Course Precondition
none
Resources
Sorhun, R., 2022. Python ile Makine Öğrenmesi, Abaküs Kitap.
Notes
Uğuz, S., 2021. Makine Öğrenmesi Teorik Yönleri Ve Python Uygulamaları İle Bir Yapay Zeka Ekolü, Nobel Akademik Yayıncılık
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Implements the Python Programming Language |
| LO02 | Do classification with Python |
| LO03 | Makes Model Estimation/Prediciton with Python |
| LO04 | Makes Data Analysis with Python |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Emphasize the importance of Statistics in life | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Define basic principles and concepts in the field of Law and Economics | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Produce numeric and statistical solutions in order to overcome the problems | 5 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Use proper methods and techniques to gather and/or to arrange the data | 5 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Utilize computer systems and softwares | 4 |
| PLO07 | Bilgi - Kuramsal, Olgusal | Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events | 5 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Apply the statistical analyze methods | 5 |
| PLO09 | Bilgi - Kuramsal, Olgusal | Make statistical inference(estimation, hypothesis tests etc.) | 2 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Generate solutions for the problems in other disciplines by using statistical techniques | 4 |
| PLO11 | Bilgi - Kuramsal, Olgusal | Discover the visual, database and web programming techniques and posses the ability of writing programme | 1 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Construct a model and analyze it by using statistical packages | 4 |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Distinguish the difference between the statistical methods | 5 |
| PLO14 | Beceriler - Bilişsel, Uygulamalı | Be aware of the interaction between the disciplines related to statistics | 3 |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Make oral and visual presentation for the results of statistical methods | 3 |
| PLO16 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Have capability on effective and productive work in a group and individually | 5 |
| PLO17 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs | 1 |
| PLO18 | Yetkinlikler - Öğrenme Yetkinliği | Develop scientific and ethical values in the fields of statistics-and scientific data collection | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Using NumPy, Pandas, and Matplotlib Libraries | Source Reading | |
| 2 | Learning Types: Supervised Learning, Unsupervised Learning, Semi Supervised Learning | Source Reading | |
| 3 | Data Preprocessing with Python | Source Reading | |
| 4 | Regression Analysis: Multiple Regression, Polynomial Regression | Source Reading | |
| 5 | Support Vector Machine | Source Reading | |
| 6 | Decision Tree | Source Reading | |
| 7 | Random Forest | Source Reading | |
| 8 | Mid-Term Exam | Source Reading | |
| 9 | Evaluation and Comparison Methods | Source Reading | |
| 10 | Regression Analysis: Logistic Regression | Source Reading | |
| 11 | Clustering | Source Reading | |
| 12 | K-Nearest Neighbor Algortihm | Source Reading | |
| 13 | Data Science with Python | Source Reading | |
| 14 | Building Predictive Models with Python | SourceReading | |
| 15 | Building Classification-Based Models with Python | Source Reading | |
| 16 | Term Exams | Source Reading | |
| 17 | Term Exams | Source Reading |
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 | 1 | 6 | 6 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 120 | ||
| Total Workload / 25 (h) | 4,80 | ||
| ECTS | 5 ECTS | ||