SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr
Lisätiedot
Lectures and exercises in English.
Vastuuhenkilö
Joni Kämäräinen
Opetus
| Toteutuskerta | Periodi | Vastuuhenkilö | Suoritusvaatimukset |
| SGN-13006 2017-01 | 2 |
Joni Kämäräinen |
Final examination and exercises. |
Osaamistavoitteet
This course will provide broad introduction to the fields of Pattern Recognition (PR) and Machine Learning (ML). The course is strongly programming oriented concentrating on models of learning (data structures to establish these models) and methods of learning (algorithms to modify data structures according to training data). After the course students will know the main approaches to machine learning starting from early ideas to the most recent ones and along with their pros and cons. Moreover, students will obtain skills to implement the most basic methods.
Sisältö
| Sisältö | Ydinsisältö | Täydentävä tietämys | Erityistietämys |
| 1. | Basic work flow in pattern recognition and machine learning. Linear models of regression and classification as the starting point. | ||
| 2. | Concept learning. | ||
| 3. | Decision tree learning | Random forests | |
| 4. | Bayesian learning and probability density estimation | ||
| 5. | Prolog language and the principal idea of inductive logic programming. | ||
| 6. | Multi-layer perception neural networks and support vector machines. | ||
| 7. | Unsupervised learning including clustering, self-organising map and linear methods (principal component analysis) | ||
| 8. | Pattern recognition and machine learning in robotics and re-inforcement learning. |
Ohjeita opiskelijalle osaamisen tasojen saavuttamiseksi
You must actively participate the lectures and do the exercises. In particular, familiarize yourself with the exercise questions before the exercise session.
Arvosteluasteikko:
Numerical evaluation scale (0-5)
Osasuoritukset:
Oppimateriaali
| Tyyppi | Nimi | Tekijä | ISBN | URL | Lisätiedot | Tenttimateriaali |
| Book | Elements of Statistical Learning, 2nd edition | Trevor Hastie, Robert Tibshirani, Jerome Friedman | Covers all the required methods, but is rather statistical approach. Mainly the random forest part is taken from this book. | Yes | ||
| Book | Machine Learning | Tom Mitchell | 0070428077 | Contents of many lectures follow this book | Yes | |
| Book | Statistical Pattern Recognition, 3rd Edition | Andrew R. Webb, Keith D. Copsey | 978-0-470-68227-2 | Very good book about the topic from practioners. Mainly the support vector machines part is taken from this book. | Yes |
Tietoa esitietovaatimuksista
No mandatory requirements, but it is assumed that the student has good knowledge of BSc level engineering mathematics and programming.
Vastaavuudet
| Opintojakso | Vastaa opintojaksoa | Selite |
| SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr | SGN-2506 Introduction to Pattern Recognition, 4 cr | |
| SGN-13006 Introduction to Pattern Recognition and Machine Learning, 5 cr | SGN-13000 Introduction to Pattern Recognition and Machine Learning, 5 cr |