Παρουσίαση/Προβολή

(DIT186) -  ΙΩΑΝΝΗΣ ΜΟΣΧΟΛΙΟΣ

Περιγραφή Μαθήματος

General information

• when: every Thursday @ 18:00 (GMT + 3)
• slides to be uploaded on eClass

Instructor

• Ioannis Moscholios (idm@uop.gr)

Books

• Probability for Machine Learning – Discover how to harness uncertainty with Python
• Doing Bayesian Data Analysis
• Bayesian Reasoning and Machine Learning
• Data Mining and Predictive Analytics

Marking scheme

• Exercises (10%)
• Final project (40%)
• Final exam: (50%) (grade >= 6)
• To pass the course: average grade >=6

Lectures

1. Review on basic probability theorems
2. Discrete and continuous random variables
3. Bayesian inference and the posterior distribution
4. Point estimation, hypothesis testing, and the MAP Rule
5. Bayesian least mean squares estimation
6. Bayesian linear least mean squares estimation
7. Statistical inference
8. Classical parameter estimation
9. Linear regression
10. Binary hypothesis testing
11. Significance testing
12-13. Introduction to multivariate models

Exams

• (to be announched)

 

Ημερομηνία δημιουργίας

Τρίτη 20 Οκτωβρίου 2020