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Course unit
STATISTICAL LEARNING 1 (MOD. A)
SCP7079227, A.A. 2017/18
Information concerning the students who enrolled in A.Y. 2017/18
Integrated course for this unit
ECTS: details
Type |
Scientific-Disciplinary Sector |
Credits allocated |
Core courses |
SECS-S/01 |
Statistics |
6.0 |
Mode of delivery (when and how)
Period |
Annual |
Year |
1st Year |
Teaching method |
frontal |
Organisation of didactics
Type of hours |
Credits |
Hours of teaching |
Hours of Individual study |
Shifts |
Lecture |
6.0 |
48 |
102.0 |
No turn |
Start of activities |
02/10/2017 |
End of activities |
15/06/2018 |
Examination board
Examination board not defined
Common characteristics of the Integrated Course unit
Prerequisites:
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basic probability theory; multivariable calculus; linear algebra; basic computing skills |
Target skills and knowledge:
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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:
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written test |
Assessment criteria:
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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:
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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:
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Lectures and Laboratories |
Additional notes about suggested reading:
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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) |
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Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction. --: Springer, 2001.
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Lavine, M., Introduction to Statistical Thought. --: None, 2013. http://people.math.umass.edu/~lavine/Book/book.html
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