SGN-41007 Pattern Recognition and Machine Learning, 5 cr

Lisätiedot

For more details, see last year slides and videos at http://www.cs.tut.fi/kurssit/SGN-41007/

The course substitutes earlier course SGN-41006 Signal Interpretation Methods. Only one of SGN-41006 and SGN-41007 can be accepted.
Kurssi korvaa aiemman kurssin SGN-41006 Signal Interpretation Methods. Vain toinen kursseista SGN-41006 ja SGN-41007 voidaan suorittaa.

Suitable for postgraduate studies.

Vastuuhenkilö

Heikki Huttunen

Opetus

Toteutuskerta Periodi Vastuuhenkilö Suoritusvaatimukset
SGN-41007 2019-01 2 Heikki Huttunen
Accepted exercises, course assignment and final exam.

Osaamistavoitteet

Students understand principles of selected statistical, pattern recognition and machine learning approaches in signal processing related problems. Student can apply the methods to real problems using modern Python tools such as Scikit-Learn and Keras. For more details, see last year slides and videos at http://www.cs.tut.fi/kurssit/SGN-41007/

Sisältö

Sisältö Ydinsisältö Täydentävä tietämys Erityistietämys
1. Statistical Signal Processing: Estimation theory; Maximum likelihood; Estimation of signal parameters (e.g., phase, amplitude and frequency).     
2. Detection theory; Receiver Operating Characteristics; Neyman-Pearson decision rule and relation to machine learning.     
3. Linear models: regression and classification, support vector machines, logistic regression, regularization.     
4. Modern tools: Random forests, Bagging, Boosting, Stacking, Deep Learning     
5. Performance evaluation, cross-validation, bootstrapping     
6. Implementations in Python: 1) Scikit-learn, 2) Keras     

Ohjeita opiskelijalle osaamisen tasojen saavuttamiseksi

Accepted exercises and assignment. Final exam.

Arvosteluasteikko:

Numerical evaluation scale (0-5)

Osasuoritukset:

Completion parts must belong to the same implementation

Oppimateriaali

Tyyppi Nimi Tekijä ISBN URL Lisätiedot Tenttimateriaali
Lecture slides   Pattern Recognition and Machine Learning   Heikki Huttunen         Yes   

Esitietovaatimukset

Opintojakso P/S Selite
SGN-13006 Introduction to Pattern Recognition and Machine Learning Mandatory   1

1 . Either SGN-13000 or SGN-13006 is a prerequisite.

Tietoa esitietovaatimuksista
The students are assumed to have the basic skills in probability, matrices and programming. Also the fundamentals of ML theory (SGN-13000 or SGN-13006) is strongly recommended.



Vastaavuudet

Opintojakso Vastaa opintojaksoa  Selite 
SGN-41007 Pattern Recognition and Machine Learning, 5 cr SGN-41006 Signal Interpretation Methods, 4 cr  

Päivittäjä: Huttunen Heikki, 30.09.2019