Predictive modeling and machine learning
Informacje ogólne
Kod przedmiotu: | 2400-SU2TS59 |
Kod Erasmus / ISCED: |
14.3
|
Nazwa przedmiotu: | Predictive modeling and machine learning |
Jednostka: | Wydział Nauk Ekonomicznych |
Grupy: | |
Punkty ECTS i inne: |
(brak)
|
Język prowadzenia: | angielski |
Rodzaj przedmiotu: | seminaria magisterskie |
Skrócony opis: |
In this seminar students will work, under supervision, on master thesis in the field of machine learning and predictive modeling. Participants will get to know current state of research in machine learning – methodology, tools and most recent literature. During the seminar all students will set a hypothesis and research plan for their master thesis. * The date of meetings may change during the semester |
Pełny opis: |
In this seminar students will work, under supervision, on master thesis in the field of machine learning and predictive modeling. Participants will get to know current state of research in machine learning – methodology, tools and most recent literature. The subject of students’ thesis will be set in the field of machine learning, for example: - methods of model optimization and parameter tuning - efficiency us state of the art model implementations - optimal model specification and and design (for instance neural networks architecture in business problems domain) This seminar is for students who are passionate in machine learning, aspiring to career in academia and are willing to devote time and resources to write a master thesis at the highest level. During the seminar all students will set a hypothesis and research plan for their master thesis. * The date of meetings may change during the semester. |
Literatura: |
Harrington, Peter. Machine learning in action. Vol. 5. Greenwich, CT: Manning, 2012. Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. "The Elements of Statistical Learning: Data Mining, Inference, and Prediction." Springer Series in Statistics ( (2009). |
Efekty uczenia się: |
KW01, KW02, KW03, KU01, KU02, KU03, KK01, KK02, KK03 |
Metody i kryteria oceniania: |
Students will be assessed by continuous evaluation of their work and progress. In every semester student must achieve a substantial milestone in their work (literature review, written and testes code base, empirical results etc.) |
Właścicielem praw autorskich jest Uniwersytet Warszawski, Wydział Nauk Ekonomicznych.