
Course unit
STATISTICAL LEARNING 1 (MOD. A)
SCP7079227, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2018/19
Integrated course for this unit
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Core courses 
SECSS/01 
Statistics 
6.0 
Course unit organization
Period 
Annual 
Year 
1st Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Lecture 
6.0 
48 
102.0 
No turn 
Examination board
Examination board not defined
Common characteristics of the Integrated Course unit
Prerequisites:

basic probability theory; multivariable calculus; linear algebra; basic computing skills 
Target skills and knowledge:

become familiar with statistical thinking; gain adequate proficiency in the development and use of standard statistical inference tools; be able to analyse datasets using a modern programming language such as R 
Examination methods:

written test 
Assessment criteria:

the successful student should show knowledge of the key concepts, skills in the analysis of data and competency in applications 
Specific characteristics of the Module
Course unit contents:

Part 1: Modes of Inference
 Data: summary statistics, displaying distributions; exploring relationships
 Likelihood: the likelihood, likelihood for several parameters
 Estimation: maximum likelihood estimation; accuracy of estimation; the sampling distribution of an estimator; the bootstrap
 Hypothesis testing
 Other approaches to inference 
Planned learning activities and teaching methods:

Lectures and Laboratories 
Additional notes about suggested reading:

Applications can be found in:
 Nolan, D.A. & Speed, T. (2000). Stat Labs: Mathematical Statistics Through Applications. Springer.
 Torgo, L. (2011). Data Mining with R: Learning with Case Studies. Chapman & Hall/CRC.
Methods for specific fields of applications can be found in the following books:
Campbell, R.C. (1989). Statistics for Biologists (3rd ed.). Cambridge University Press.
Devore, J.L. (2000). Probability and Statistics for Engineering and the Sciences (5th ed.). Duxbury Press, Pacific Grove, CA.
Agresti, A. & Finlay. B. (2007). Statistical Methods for the Social Sciences (4th ed.). Prentice Hall 
Textbooks (and optional supplementary readings) 

Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction. : Springer, 2001.

Lavine, M., Introduction to Statistical Thought. : None, 2013. http://people.math.umass.edu/~lavine/Book/book.html


