Syllabus
STAT 430X: EMPERICAL METHODS FOR COMPUTER SCIENCE
RESEARCH
INLECTURES: MWF 1:10 – 2:00 PM, PEARSON 1106
INSTRUCTOR: Arka P. Ghosh (apghosh@iastate.edu)
303 Snedecor Hall, 515-294-2240
OFFICE HOURS: 2:00-
3:00 pm Monday and Wednesday
TEXT: We will not follow any fixed text for this experimental course.
SOFTWARE: We will use R - it can be downloaded from: http://www.r-project.org/
GRADES:
Assignments: 55 pts (as assigned in class) – lowest score dropped.
Exam
I:
25
pts Feb 20 (in class)
Exam
II:
25
pts Apr
3 (in class)
Final
Exam/Term Project: 45 pts
May 5 (9:45-11:45 a.m, tentative – if in class
exam)
. 16 points : project report
10
points : project presentation
12
points : evaluation from peers (classmates) – based on presentation –
(trimmed mean – lowest and highest scores deleted)
7
points : for attending others seminar and evaluating their presentation
(0.5 points for each presentation attended)
QUESTIONS or PROBLEMS: Contact me by email or talk to me after the
class or during office hours.
List of Topics:
1 Introduction
(Probability Theory and Random Variables)
1.1 Basic Probability, Operation of Sets, Kolmogorov’s Axioms
1.2 Counting Methods
1.3 Conditional Probabilities, Independence of Events
1.4 Bayes’ Rule, Bernoulli Experiments
1.5 Discrete Random Variables, Special Probability Mass Functions (p.m.f’s)
1.6 Continuous Random Variables, special probability density functions
1.7 Multiple Random Variables
2 Data, Sampling and Basic Statistical Inference
2.1 Data Summary, Graphical and Tabular
Representations
2.2 Sampling from a
population-Parameter and Statistics
2.3 Central Limit Theorem (CLT)
2.4
Parameter Estimation
2.5
Confidence intervals
2.6
Hypothesis Testing
2.7 Goodness of Fit Tests,
2.8
Non-Parametric tests (?)
3. Regression
3.1
Simple linear Regression
3.2
Multiple Linear Regression
3.3
Regression Diagnostics
3.4
Logistic and Poisson regression
4. Basic concepts in
experimental design and ANOVA
4.1
Randomized Block Designs
4.2
Factorial Designs
5. Elementary Simulation
5.1
Random Number Generators
5.2
Different methods for generating from distributions
5.3
Simulating from Special Distributions
6. Advanced Inference
Techniques
6.1
Randomization tests
6.2 Jack-knife
6.3
Bootstrap
6.4