Babes-Bolyai University of Cluj-Napoca
Faculty of Mathematics and Computer Science
Study Cycle: Master

SUBJECT

Code
Subject
MIG1001 Stochastic Modeling of Data
Section
Semester
Hours: C+S+L
Category
Type
Computational Mathematics - in Hungarian
3
2+1+1
speciality
compulsory
Interdisciplinary Mathematics - in Hungarian
3
2+1+1
speciality
compulsory
Optimization of computational models- in Hungarian
3
2+1+1
speciality
compulsory
Teaching Staff in Charge
Assoc.Prof. CSATO Lehel, Ph.D.,  csatolcs.ubbcluj.ro
Aims
References
[1]. Russell S, Norvig P. (2003) Artificial Intelligence: A Modern Approach (Second Edition), Prentice Hall.
[2]. Mitchell T (1997) Machine Learning, McGraw Hill.
[3]. Bernardo J.M, Smith A.F.M (2000) Bayesian Theory, John Wiley & Sons.
[4]. MacKay D.J.C (2003) Information Theory, Inference and Learning Algorithms, Cambridge University Press, HTTP: http://wol.ra.phy.cam.ac.uk/mackay/itila/book.html.
[5]. Rasmussen C.E, Williams C.K.I (2006) Gaussian Processes for Machine Learning, The MIT Press.
[6]. Rabiner L.R, Juang, B.H (1986) An introduction to Hidden Markov models, IEEE ASSP Magazine, pp: 4-15.
[7]. Durbin R, Eddy S.R, Krogh A, Mitchison G (1999) Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press.
[8]. Hyvärinen A, Karhunen J, Oja E (2001) Independent Component Analysis, Wiley-Interscience.
[9]. Barto A. (2002): Statistical Pattern Recognition, John Wiley & Sons.
Assessment
Links: Syllabus for all subjects
Romanian version for this subject
Rtf format for this subject