| Date | Topic | Literature | Presenter |
|---|---|---|---|
| 18.1 | Introduction to the course | - | Jaakko Hollmén |
| 25.1. | Lecture 1 | TBA | Jaakko Hollmén |
| 1.2. | Better subset regression using the nonnegative garrote | Breiman, Leo Technometrics, 37(4):373-384, Nov. 1995 | Seppo Fagerlund |
| 1.2. | Regression shrinkage and selection via the lasso | Tibshirani, Robert Journal of the Royal Statistical Society 58(1):267-288, 1996 | Francesco Corona |
| 8.2. | Least angle regression | B. Efron, T. Hastie, I. Johnstone, and
R. Tibshirani The Annals of Statistics, 32(2):407-499, Apr. 2004 | Timo Similä Sven Laur |
| 15.2. | Regularization theory and neural network architectures | F. Girosi, M. Jones, and
T. Poggio Neural Computation, 7(2):219-269, Mar. 1995 | Yongnan Ji Jin Hao |
| 22.2. | Atomic decomposition by basis pursuit | S. S. Chen, D. L. Donoho, and
M. A. Saunders SIAM Journal of Scientific Computing, 20(1):33-61, 1998 | Nima Reyhani Ville Mäntynen |
| 1.3. | An Equivalence Between Sparse Approximation and Support Vector Machines | F. Girosi Neural Computation, 10(6):1455-1480, Aug. 1998 | Antti Sorjamaa |
| 1.3. | Sparse regression for analyzing the development of foliar nutrient concentrations in coniferous trees | In proceedings of the
Fourth International Workshop on Environmental Applications of Machine Learning (EAML 2004) pages 57-58, Bled, Slovenia, September 2004 | Mika Sulkava |
| 1.3. | Input selection for long-term prediction of time series | - | Jarkko Tikka |
| 8.3. | 1. The Learning Methodology 2. Linear Learning Machines | An Introduction to support vector
machines (and other kernel-based learning methods) N. Cristianini and J. Shawe-Taylor Cambridge University Press | Antti Sorjamaa Nima Reyhani |
| 15.3. | 3. Kernel-Induced Feature Spaces | An introduction to SVM | Timo Similä Seppo Fagerlund |
| 22.3. | 4. Generalisation Theory | An introduction to SVM | Jan-Hendrik Schleimer Ramunas Girdziusas |
| 29.3. | EASTER HOLIDAY | - | - |
| 5.4. | 5. Optimisation Theory 6. Support Vector Machines | An introduction to SVM | Yongnan
Ji Jin Hao |
| 12.4. | 7. Implementation
Techniques 9. Pseudocode for the SMO Algorithm | An introduction to SVM | Amaury Lendasse |
| 19.4. | 8. Applications of Support Vector Machines | An introduction to SVM | Matthieu
Molinier Sven Laur |