Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Masterat
Domeniul: Informatica
Programul de studii: Optimizarea modelelor informatice - în limba maghiară
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MIH1010	Adaptive Data Processing 	

Semester	Hours: C+S+L	Category	Status	
4	2+0+2	speciality	optional	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
CSATO Lehel	Ph.D.	Assoc.Prof.	Programming  Languages and Methods	*			

Associated faculty members
Name and	Scientific	Institution	Type of	Type of activity	
surname	title		position	C	S	L	

III. Course objectives
The lecture presents the notions related to adaptive data processing, methods that are based on basic probability and modern machine learning algorithms. We present methods to classify data, to perform regression or clustering on various data types.

IV. Course contents
Stochastic data modeling is the application to various types of data of algorithms that exploit the rules of probability and allow direct representation of the stochastic nature of the real world. In order to extract information, usually one has to include prior knowledge into the algorithm. The inclusion of the prior knowledge is possible via the rules of probability. Prior knowledge is the model we want to use. If there are huge data-sets available, then one must use statistical methods, for a few data a lot more emphasis is to be put on the model. The algorithms depend also on the type of the problem we want to solve. The course is aimed as a practical introduction to various models from machine learning and to familiarize the students with the use of randomness and stochastic models.

V. Bibliography
[1]. Russell S, Norvig P. (2003) Artificial Intelligence: A Modern Approach (Second Edition), Prentice Hall.
[2]. Bishop C.M. (2006) Pattern Analysis and Machine Learning, Springer Verlag.

VI. Thematic of didactic activities per weeks
Thematic overview of the lectures:

1. Component analysis (weeks 1-4) ref. [2,4,8,9]:
   1.1 Basics of statistical modelling &#8211; matrices and eigenvalues [9],
   1.2 Principal components used in signal de-noising [2,4].
   1.3 Independent components used in blind source separation of signals [8].

2. Bayesian modelling (weeks 5-7) ref. [1,2,3,4,5]:
    2.1. Introduction to various estimation methods [1, 2, 3],
    2.2. Hierarchical model specification and parameter estimation [1,2,4],
    2.3. Bayesian modelling: obtaining the posterior distribution [4, 5].

3. Hidden Markov models (HMMs) weeks (8-11) ref. [7,8]:
    3.1. HMM definitions [7],
    3.2. Estimating the latent states [7],
    3.3. Applications of HMMs (1) speech recognition [7,8],
    3.4. Applications of HMMs (2) gene segmentation [7,8].

4. Gaussian Process models (weeks 12-14) ref. [5,6]:
    4.1. Joint Gaussians, kernel functions, Functional Bayesian models [4,5,6],
    4.2. Approximations: using the KL-projection, sparse approximations,
    4.3. Applications of Gaussian process inference.

VII. Didactic methods used
I will encourage individual work through additional exercises that are given during a lecture and discussed in seminars. This allows students the individual work. The laboratory exercises are chosen so that the students get a grip on the method they choose to apply to a data set.

VIII. Assessment
The exam will consist of  (40%) a presentation of a topic chosen in the first 6 weeks of the semester, (20%) the solution of the laboratory examples, (40%) oral examination based on the topics of the lectures and the seminars presented by the students.

IX. Additional references
[9]. Barto A. (2002): Statistical Pattern Recognition, John Wiley & Sons.
[3]. Bernardo J.M, Smith A.F.M (2000) Bayesian Theory, John Wiley & Sons.
[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.
[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.
[4]. Mitchell T (1997) Machine Learning, McGraw Hill.
[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.

	Dean,							Chair head,

	Date,							Course responsible,
								Assoc.Prof. CSATO Lehel, Ph.D.