Statistics for Computer Science - Teaching Organization
RNDr. Radim Navrátil, Ph.D.
Statistics for Computer Science - Teaching Organization
RNDr. Radim Navrátil, Ph.D.
Statistics for Computer Science - Teaching Organization
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jaro 2024

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During the last week of the semester (May 13-17), Test 2 will take place. As explained in the lectures, the use of Chatbots will be prohibited for this test. The test will cover the topics discussed in lectures 6-10 and corresponding seminars.

On Tuesday, May 21 (14:00 and 14:30), there will be exactly one makeup opportunity for those who could not attend the tests but have properly excused their absence (on time in Information System). Please, sign in.


Course outline

  • Introduction to statistics. Data preprocessing
  • Exploratory data analysis (descriptive statistics, data visualization)
  • Principal component analysis
  • Brief review of probability theory
  • Probability distributions. Central limit theorem
  • Point estimates (maximum likelihood method)
  • Confidence intervals, testing of statistical hypotheses
  • Model selection. Normality testing
  • Testing of statistical hypotheses II (two sample tests)
  • ANOVA, tests of independence, contingency tables
  • Nonparametric tests
  • Linear regression model (model description, assumptions, interpretation)
  • Modern methods - Monte Carlo simulations, bootstrap
  • Revision


Prerequisities

  • Basic knowledge of mathematical analysis: functions, limits of sequences and functions, derivations and integral for real and multidimensional  functions.
  • Basic knowledge of linear algebra: matrices and determinants, eigenvalues and eigenvectors.
  • Basic knowledge of probability theory: probability, random variables and vectors, limit theorems.
  • Basic knowledge of linear regression models.


Review (self study) of probability theory

  • Sigma algebra, probability measure and its properties, Kolmogorov definition of probability, conditional probability, independent events.
  • Random variables and vectors, their distributions, properties and connections. Discrete and continuous random variables and random vectors, their distributions. Numerical characteristics of random variables and random vectors. Independent random variables.
  • Law of large numbers, central limit theorem.
    Chyba: Odkazovaný objekt neexistuje nebo nemáte právo jej číst.
    https://is.muni.cz/el/fi/jaro2024/MV013/um/literature/All_of_Statistics__A_Concise_Course_in_Statistical_Inference__Springer_Texts_in_Statistics____PDFDrive__.pdf
    Chyba: Odkazovaný objekt neexistuje nebo nemáte právo jej číst.
    https://is.muni.cz/el/fi/jaro2024/MV013/um/literature/casella_berger_statistical_inference1.pdf
  • If students do not have sufficient knowledge the above terms, they have to self-study it.
  • To understand the content of the course, it is essential to master probability theory.
  • Week 3 - 4 : A brief overview of the above terms.
  • From that on, we will assume that all the students know it, even with all details not mentioned in the lectures.


    • Assessment methods

    • 2 homework assignments (20 points in total).
    • 2 short test at the seminar (20 points in total).
    • Final written exam  (60 points in total).

    Type of Completion:

    • Fulfilling requirements  (Zápočet, Z) - need to get at least 50 points in total.
    • Examination (Zkouška, Zk) - grading:

    100-90A
    89-80B
    79-70C
    69-60D
    59-50E
    49-0F


    The final exam

    Exam2024
    PDF ke stažení
    • The exam has two parts: 
      • Data analysis (open notes, 30 points)
      • Explaining the theory (closed notes, 30 points)

    • Time for the exam: 60+60 minutes.
    • You need to get at least 10 points from each parts to pass.
    • During the exam, you will be asked to prove your identity (showing your ID).
    • If anybody has recorded disability in Information System and needs special treatment, let me know before the exam (in advance). Subsequent demands will not be taken into account.
    • The examination language is English.
    • In case of suspicious solutions and results, you might be subject to an additional oral exam.

    Seminars

    • Attendance at the seminars is obligatory (two unexcused absences will be tolerated).
    • Only proper excuses will be accepted uploaded into Information System on time).
    • All the students must be assigned to a seminar group ( otherwise, an "X" will be received).
    • There are 2 groups for students who cannot bring their own laptops and 4 groups for students with their own laptops.


    Homework assignments

    • The assignment will be published centrally on Monday (2 times a semester, not regularly). 
    • Deadline: the next Wednesday 23:59 (midnight).
    • Solution (pdf and source code) upload to "Homework vaults" in the Information system.
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