AI and machine learning for finance | UniBG Economics & Finance
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AI and machine learning for finance

Year: 1
Semester: 2
Lecturer: Prof. Michela Cameletti

Hours: 48
ECTS: 6

Educational Goals

The course aims at providing the knowledge of cutting-edge AI and machine learning (ML) tools for modeling financial data defined in high-dimensional spaces and characterised by non-linear relationships. In particular, the objective of the considered methods is the automatic detection of patterns in the data (i.e., to “learn” from data) by taking into account the specific peculiarities of financial data. The analysts and investors can then use the estimated models to make decisions and choose investment strategies under uncertain and risky conditions.
 
At the end of the course, the student will gain the ability to:

  1. Understand and explain the main machine learning and deep learning techniques.
  2. Choose and apply the appropriate modeling tool, in the class of ML methods, for the analysis of financial data.
  3. Use free open-source software R and Python to perform data analysis and visualization and implement ML models.
  4. Assess the performance of the implemented predictive methods and interpret all the available results from a decision-making perspective.
Course Content
  • Introduction to AI and machine learning: supervised, unsupervised and reinforcement learning, deep-learning, regression and classification problems, the bias-variance trade-off.
  • Illustration of financial applications with machine learning methods (e.g., price prediction, portfolio construction, risk analysis, credit ratings, outlier detection, algorithmic trading). 
  • Training, validation and testing, cross-validation back-testing, hyper-parameter tuning.
  • Ensemble methods: classification and regression trees, bagging, random forest, boosting.
  • Neural networks: feedforward convolutional and recurrent neural network.
  • Elements of reinforcement learning.
Teaching Methods

The course consists of theory lectures and R/Python lab sessions (usually R labs represent 1/4 of the total number of hours).

Assessment and Evaluation

The exam consists of:

  • A test including open-ended and T/F questions concerning theoretical topics or short applications of the studied methods.
  • Exercises to be solved using the R/Python software in order to evaluate the ability of the student in analysing data and interpreting outputs. 

The two parts of the exam (theoretical and practical) are each worth 50% of the total score, approximately.