Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Licenţă
Domeniul: Calculatoare şi tehnologia informaţiei
Programul de studii: Ingineria informatiei - in limba engleza
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MMP0007	Experimental Data Processing	

Semester	Hours: C+S+L+P	Category	Status	
8	2+1+2+0	speciality	compulsory	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
TRÎMBITAS Radu Tiberiu	Ph.D.	Assoc.Prof.	Applied Mathematics	*			

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

III. Course objectives
To introduce students to problems and methods of Experimental Data Processing	

IV. Course contents
1. Errors. Measuring and classification.
2. Interpolation of data. Polynomial interpolation(Lagrange, Hermite). Efficient computing of interpolation polynomials. Divided differences. Spline interpolation. B-splines, cubic splines.
3. Least squares approximation. Normal equations and orthogonal Systems. Orthogonal polynomials. Fast Fourier transform.
4. Linear regression. Linear models and prediction. Inferences on model and coefficients. Curve fitting.
5. Generalized linear models.  
6. Multivariate statistics: Principal component analysis, clusters, factor analysis.
7. Data visualization. 2D and 3D Graphics. Volume visualization techniques.	

V. Bibliography
P. Blaga - Statistica prin … MATLAB, Presa Universitară Clujeană, Cluj-Napoca 2003
D.Ciurchea, V.Chiş - Prelucrarea datelor experimentale, Litografia UBB, Cluj-Napoca, 1995.
W.H. Press, B.P. Flannery, S.A. Teulkolsky, W.T. Vetterling - Numerical Recipes, third edition, Cambridge Univ. Press, New York, 2007. 
R. Trîmbiţaş - Metode statistice, Presa Universitară Clujeană, Cluj-Napoca, 2000	

VI. Thematic of didactic activities per weeks
Week 1. Lecture: Error Theory I. Seminar: Errors and Floating point Arithmetic. Laboratory: Errors and FPA
Week 2. Lecture: Polynomial Interpolation (Lagrange, Hermite). Laboratory: Lagrange Interpolation.
Week 3. Lecture: Efficient Computing of Interpolation Polynomials. Divided Differences. Seminar: Polynomial Interpolation. Laboratory: Newton Form of Lagrange Polynomial. Hermite Interpolation.
Week 4. Lecture: Spline Interpolation. B-splines. Laboratory: Cubic and B-splines.
Week 5. Lecture: Least squares approximation. Normal equations and orthogonal systems.
Seminar: least squares. Laboratory: Least squares approximation.
Week 6. Lecture: Fast Fourier Transform. Laboratory: FFT
Week 7. Lecture: Estimation and Hypothesis Testing. Seminar: Statistics. Estimation and Hypothesys Testing. Laboratory: Maximum Likelihood Method and Statistical Tests. 
Week 8. Lecture: Linear regression. Linear Models and Prediction. Curve fitting. Laboratory: Linear Models and Curve Fitting.
Week 9. Lecture: Generalized linear models.  Seminar: Curve Fitting and Generalized Linear Models.
Laboratory: Curve fitting (continued) and Generalized Linear Models.
Week 10. Lecture: Multivariate statistics I: Principal Component Analysis, Factor analysis. Laboratory: Multivariate statistics I. Laboratory: Multivariate statistics I. 
Week 11. Lecture: Multivariate statistics II: clusters, classification. Seminar: Multivariate Statistics.
Laboratory: Multivariate statistics II.
Week 12. Lecture: Data visualization. 2D and 3D Graphics. Volume visualization techniques.
Laboratory: Data Visualization.	

VII. Didactic methods used
Expositions: description, explanation, class lectures, dialog-based lectures, lectures with demonstrations, introductive lectures, current lectures, synthesis lectures. 
Conversations: debate, dialog, introductive conversations, conversations for knowledge consolidation, conversations to systematize and synthesize knowledge 
Use of problems: use of problem questions, problems and problem situations. 
Discovery: directed and independent rediscovery, creative discovery, inductive discovery, deductive discovery, discovery by documenting, experimental discovery. 
Other methods: case study; cooperation, simulation, brainstorming, individual study, exercise, homework study.	

VIII. Assessment
Exam: written paper and practical test.	

IX. Additional references

	Dean,							Chair head,

	Date,							Course responsible,
							Assoc.Prof. TRÎMBITAS Radu Tiberiu, Ph.D.