Pika Povh Mavrič and Nemanja Cvetić

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On Monday, August 3, 2026, at 4:00 PM, two lectures will be held as part of the Monday RIN seminar, organized by the Departments of Information Sciences and Technologies UP FAMNIT and UP IAM.

Time/Location: August 3, 2026, at 4:00 PM, via Zoom.

Lecture 1:
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Lecturer: Pika POVH MAVRIČ
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Pika Povh Mavrič is currently completing her master’s studies in Computer Science at UP FAMNIT. She works as a data analyst in the Customer Relationship Management department at a bank, where she applies and deepens her expertise in data analytics. Her work involves automating processes, improving data quality, creating various reports and analyses, and applying data-driven decision-making to everyday processes within the department. The topic of her paper aligns well with her work in the banking sector.

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Title: Machine learning models for predicting personal loan acquisition
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Summary:

In modern retail banking, the traditional approach of mass marketing consumer loans is becoming highly inefficient and often leads to customer fatigue. To address this issue, this research project focuses on developing a machine learning model designed to accurately predict a customer’s intent to acquire a personal loan. By analyzing historical data, including demographics, product ownership, and past marketing engagement, the project uncovers hidden behavioral patterns. This predictive approach allows financial institutions to shift towards a more customer-centric strategy, optimizing marketing resources and offering loans only to those who truly need them.

The seminar will be conducted in English.

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Lecture 2:

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Lecturer: Nemanja CVETIĆ
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Nemanja Cvetić is a Master’s student in Computer Science with a Bachelor’s degree in Information Technology. His research interests include machine learning, explainable artificial intelligence (XAI), data science, computer science and software engineering. His current research focuses on evaluating model interpretability techniques and their practical applications in machine learning.

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Title: Model interpretability in machine learning: A Comparison of LinearSHAP TreeSHAP, and LIME on the California Housing Dataset
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Summary:

This seminar presents an empirical comparison of SHAP (LinearSHAP and TreeSHAP) and LIME as post-hoc explainability methods for machine learning models. Using Linear Regression and Random Forest models trained on the California Housing dataset, the study evaluates the quality, consistency, and interpretability of feature attributions. The results demonstrate that SHAP provides more stable and theoretically grounded explanations, while LIME shows greater variability, particularly for complex non-linear models.

The seminar will be conducted in English.

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The seminars will be held online via Zoom, starting at 4:00 PM, using the following link:

https://upr-si.zoom.us/j/297328207?pwd=S3Zpdk1VR3pjckNtWkQwKzlvcDR5UT09

Meeting ID: 297 328 207
Passcode: 123456789

You are kindly invited!