Akademik Dersler - Tanıtım

Course Introduction Information


Code - Course Title	YZM3004 - Applied Machine Learning
Course Type	Required Courses
Language of Instruction	İngilizce
Laboratory + Practice	3+2
ECTS	6.0
Course Instructor(s)	DOKTOR ÖĞRETİM ÜYESİ NİHAL İNCE
Mode of Delivery	This course is offered exclusively in an in-person format.
Prerequisites	There are no prerequisites or co-requisites for this course.
Courses Recomended	There are no other recommended courses related to this one.
Required or Recommended Resources	Huyen, C. (2022). Designing machine learning systems: An iterative process for production-ready applications. O'Reilly Media.McMahon, A. P. (2021). Machine learning engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples. Packt Publishing.Treveil, M., Ommvenkat, K., Duplessis, C., Lefevre, C., & Dataiku Team. (2020). Introducing MLOps: How to scale machine learning in the enterprise. O'Reilly Media.
Recommended Reading List	Huyen, C. (2022). Designing machine learning systems: An iterative process for production-ready applications. O'Reilly Media.McMahon, A. P. (2021). Machine learning engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples. Packt Publishing.Treveil, M., Ommvenkat, K., Duplessis, C., Lefevre, C., & Dataiku Team. (2020). Introducing MLOps: How to scale machine learning in the enterprise. O'Reilly Media.
Assessment methods and criteria	1 midterm exam, 1 project, 1 final exam
Work Placement	This is not suitable for this class.
Sustainability Development Goals

Content

Weeks	Topics
Week - 1	The ML Lifecycle: MLOps. CRISP-DM.
Week - 2	Advanced Feature Engineering: Pipeline Automation.
Week - 3	Imbalanced Datasets. The SMOTE Technique.
Week - 4	Feature Selection Methods (Filter, Wrapper, Embedded).
Week - 5	Model Deployment: Model Serialization (pickle, joblib).
Week - 6	The REST API Concept. Building a Simple API with Flask.
Week - 7	Serving an ML Model as an API with FastAPI.
Week - 8	Containerization: Introduction to Docker. Why Docker? Writing a Dockerfile.
Week - 9	Building and running the ML API as a Docker image.
Week - 10	Introduction to MLflow. Logging Parameters and Metrics.
Week - 11	Model Monitoring: Concept Drift and Data Drift.
Week - 12	Explainable AI: The "Black Box" Problem. Model-Agnostic Methods.
Week - 13	SHAP (SHapley Additive exPlanations).
Week - 14	Final Project Presentations.

Learning Activities and Teaching Methods

Teaching Methods
Lecture
Discussion
Question & Answer
Team/Group Work
Drill - Practise
Problem Solving
Report Preparation and/or Presentation
Competences
Productive
Rational
Questoning
Follow ethical and moral rules
Use time effectively
Eleştirel düşünebilme
Abstract analysis and synthesis
Problem solving
Information Management
Elementary computing skills
Decision making
Project Design and Management

Assessment Methods

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
Toplam (%)		0