Information Package / Course Catalogue - Adnan Menderes University

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Machine Learning With Financial Applications

Course Code	: EFN539
Course Type	: Area Elective
Couse Group	: Second Cycle (Master's Degree)
Education Language	: Turkish
Work Placement	: N/A

Theory	: 3
Prt.	: 0
Credit	: 3
Lab	: 0
ECTS	: 5

Objectives of the Course

The aim of this course is to provide students with the ability to apply machine learning methods on financial data. The focus is on the use of machine learning algorithms in areas such as data analytics, forecasting, portfolio optimization and risk analysis in financial markets.

Course Content

This course is an applied program that uses machine learning techniques to solve problems related to financial markets. Major topics such as financial time series, credit risk analysis, portfolio optimization and fraud detection are covered. Data preprocessing, modeling, visualization and interpretation skills are acquired using the Python programming language. Practical examples with real data sets and a comprehensive project presentation is expected at the end of the semester.

Name of Lecturer(s)

Assoc. Prof. Elvan HAYAT

Learning Outcomes

1.	Analyze the properties of financial datasets.
2.	Apply machine learning algorithms to financial problems.
3.	To be able to do financial modeling in Python environment.
4.	Compare and interpret prediction models and classification methods.
5.	Develop data-driven recommendations for financial decision support systems.

Recommended or Required Reading

1.	Marcos López de Prado (2018). Advances in Financial Machine Learning, Wiley.
2.	Yves Hilpisch (2020). Artificial Intelligence in Finance, O’Reilly.
3.	Geron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly.

Weekly Detailed Course Contents

Week 1 - Theoretical

Introduction Financial Technology and Data Science

Week 2 - Theoretical

Introduction to Financial Data with Python

Week 3 - Theoretical

Financial Time Series Data and Features

Week 4 - Theoretical

Regression Models and Stock Price Forecasting

Week 5 - Theoretical

Credit Risk Estimation with Classification Models

Week 6 - Theoretical

Support Vector Machines and Financial Applications

Week 7 - Theoretical

Decision Trees and Random Forests

Week 8 - Intermediate Exam

Midterm

Week 9 - Theoretical

Clustering and Anomaly Detection (Fraud Detection)

Week 10 - Theoretical

Introduction to Deep Learning: Artificial Neural Networks

Week 11 - Theoretical

Financial Time Series Forecasting with LSTM

Week 12 - Theoretical

Portfolio Optimization and Risk Management

Week 13 - Theoretical

Applied Project Work

Week 14 - Theoretical

Student Presentations and Discussions

Week 15 - Final Exam

Final Exam

Assessment Methods and Criteria

Type of Assessment	Count	Percent
Midterm Examination	1	%40
Final Examination	1	%60

Workload Calculation

Activities	Count	Preparation	Time	Total Work Load (hours)
Lecture - Theory	14	2	3	70
Individual Work	15	2	0	30
Quiz	1	9	1	10
Midterm Examination	1	14	1	15
TOTAL WORKLOAD (hours)				125

Contribution of Learning Outcomes to Programme Outcomes

	PÇ-1	PÇ-2	PÇ-3	PÇ-4	PÇ-5	PÇ-6	PÇ-7	PÇ-8	PÇ-9
OÇ-1	4	5	5	5	5	4	5	5	5
OÇ-2	5	5	4	5	5	4	3	4	4
OÇ-3	3	4	4	5	5	5	5	4	5
OÇ-4	5	5	4	4	4	4	3	5	5
OÇ-5	4	5	5	5	5	5	5	5	5

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