麻豆社

Course Topics
Topics covered: 路 Introduction to general AI concepts 路 From big data to generative AI 路 Machine learning models 路 Data preparation and collection 路 Classification and regression models 路 Ensemble learning 路 Neural networks 路 Clustering 路 Dimensionality reduction A detailed program will be provided at the beginning of the course.

Teaching format
A combination of lectures, exercises, projects, and guest speakers.

Educational objectives
ILO (Intended Learning Outcomes) ILO 1 鈥� Knowledge and Understanding: ILO 1.1 of the fundamentals of corporate finance for the correct application, for example, of decision-making models and the management of financial data and risks in treasury management ILO 1.2 of management models and cost-effectiveness of different types of intermediaries, market microstructure, operational efficiency of financial markets, and the impact of financial markets on the cost-effectiveness of intermediaries ILO 1.3 of a wide range of investment, financing, and risk management instruments, starting from the fundamentals of portfolio diversification and the classic models for asset pricing and risk measurement ILO 1.4 of specific Finance topics that characterize the profession of Financial Analyst, Portfolio Manager, Chief Financial Officer (CFO), Administrative Manager, Controller, Internal Auditor, and Business Consultant ILO 2 鈥� Applying Knowledge and Understanding: ILO 2.1 for the identification, evaluation, and management of investments in financial markets ILO 2.2 for setting coherent financial management strategies in companies or financial intermediaries, competently applying the acquired knowledge in risk management techniques, asset valuation, and derivative handling ILO 3 鈥� Making Judgments: ILO 3.1 ability to relate models and empirical evidence in the study of companies, intermediaries, and financial markets ILO 4 鈥� Communication Skills: ILO 4 Ability to effectively communicate, both orally and in writing, the specialized content of individual disciplines, using different registers depending on the audience and the communicative and educational purposes, and to assess the educational impact of one鈥檚 communication ILO 5 鈥� Learning Skills: ILO 5.1 ability to develop general models based on the phenomena studied

Assessment
The final grade will be based on a combination of written exam, project works, and active participation in the course (presentations, case study discussions, active participation during guest lectures). ILOs 1-5 assessed

Evaluation criteria
The assessment consists of four parts: 1) A written exam with multiple-choice questions on theoretical concepts discussed in the course. 2) Presentation and discussion of a paper on artificial intelligence, supported by generative AI. 3) Development of a data science project applied to the banking-financial sector. 4) Active participation during guest lectures (for attending students only).