Information
| Course Code | EIP109 |
| Semester | 1 |
| Category | Basic |
| ECTS Credits | 8 |
| Eclass |
Professors
Proposed Bibliography
- Machine Learning: A Bayesian and Optimization Perspective (Net Developers) 19 May 2015 by Sergios Theodoridis.
- Pattern Recognition and Machine Learning (Information Science and Statistics) 2007 by Christopher M. Bishop.
- The Elements of Statistical Learning Book by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie.
Course Description
k-mean clustering, fcm, vlad, Fuzzy logic
Linear regression, logistic regression, linear SVM
Non-linear regression, Non-linear classification, kernel SVM, k-NN, etc.
Image recovery techniques
Low-dimensional representations
Gabor Filters
PCA and LDA for face recognition
ICA analysis
Compression-codification-sparse representation
NMF, Archetypal analysis
Spectral clustering-Graphs – MST
Neural networks
Convolutional neural networks
3-D shapes description – analysis – classification
