| Code |
Course Name |
Language |
Type |
| BLG 454E |
Learning From Data |
English |
Compulsory /
Elective |
| Local Credits |
ECTS |
Theoretical |
Tutorial |
Laboratory |
| 3 |
5 |
3 |
0 |
0 |
| Course Prerequisites and Class Restriction |
| Prerequisites |
MAT 271 MIN DD
or MAT 271E MIN DD
or ECN 301E MIN DD
or YZV 231E MIN DD
|
| Class Restriction |
None |
| Course Description |
| Introduction to
Machine Learning, major applications
Mathematical background, marginal and conditional Probability, Bayes theorem, Bayesian
decision theory
Density estimation, Maximum Likelihood estimate, Bayesian Learning, Naïve Bayes
Linear regression
Bias-variance
dilemma, regularization, ridge regression and lasso
Linear classifiers
Artificial neural networks, perceptron and multilayer perceptron
Assessment and comparison of classifier performance
Feature selection and extraction
Large margin classifiers, support
vector machines, kernel methods
Decision trees and random forest
Unsupervised learning, clustering
Deep learning and big data |
|
