| Code |
Course Name |
Language |
Type |
| VBA 315E |
Machine Learning |
English |
Compulsory |
| Local Credits |
ECTS |
Theoretical |
Tutorial |
Laboratory |
| 3 |
7 |
3 |
0 |
0 |
| Course Prerequisites and Class Restriction |
| Prerequisites |
VBA 312E MIN DD
or END 305E MIN DD
|
| Class Restriction |
None |
| Course Description |
| This course builds upon foundational machine learning concepts, offering in-depth exploration into advanced
techniques and applications. Designed for students with prior knowledge, it covers regression, classification,
and neural networks before delving into specialized topics like Bayesian methods, graph based methods, deep
learning architectures, anomaly detection, and explainable AI. Practical applications in finance, NLP, and
recommender systems provide real-world context, preparing students to tackle complex data challenges using
cutting-edge machine learning tools. |
|
