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Date

Topics

Slides       

Matlab

Homework
(not graded)

Exams
1

Aug 26

 Why probability and statistics in comutational bioengineering? Lecture 1      
2

Aug 28

Random experiments. Sample space, Events, Venn diagramms. 

Definitions of probability:
Statistical (part 1)

 Lecture 2

coin_toss_template.m

coin_toss.m

    
3

Sep 2

Definitions of probability:
Inductive. 

Paradoxes of inductive definition of probability

Combinatorics

 Lecture 3      
4

Sep 4

Combinatorics (continued)

Probability axioms

Conditional probability

Independence of events

Circuit diagrams

Bayes' theorem

Specificity/Sensitivity of tests

 Lecture 4 circuit_template.m    
5

Sep 9

Secretary problem

Simpson's paradox

Monty Hall problem

 Lecture 5 monty_hall_template.m    
6

Sep 11

Discrete random varibales,

PMF. CDF, CCDF, 
Mean, Variance, Skewness

Uniform distribution

The distribution of the
Ct value in COVID-19
PCR tests

 Lecture 6 uniform_discrete_template.m hw1.pdf


 
7

Sep 16

Bernoulli trials

Binomial Distribution

Poisson Distribution

 Lecture 7

binomial_template.m

    
8

Sep 18

 Poisson distribution in genome assembly Lecture 8 poisson_template.m    
9

Sep 23

Geometric distribution.

Phylogenetic trees and time to the most recent common ancestor. 

Mitochondrial Eve &
Y-chromosome Adam

 Lecture 9

 

    
10

Sep 25

Negative Binomial Distribution

Cancer: Driver and Passenger genes

 Lecture 10

negative_binomial_template.m

 hw1_with_solutions.pdf  
11

Sep 30

Probability Density Function, CDF, CCDF, Mean, Variance, Std

Uniform continuous distribution. 

Constant rate (Poisson) process.

Exponential distribution.

 Lecture 11   hw2.pdf  
12

Oct 2

Erlang and Gamma distributions

Gaussian distribution

Standardizing and working with the CDF table

 Lecture 12

exponential_gamma_template.m

    
13

Oct 7

Fitting Gaussian distribution to the data for binding energies of protein-protein interactions 

Multiple random variables. Joint, Marginal, and Conditional PMFs

Statistical independence of random variables

 Lecture 13

PINT_binding_energy.m

PINT_binding_energy.mat

 hw2_with_solutions.pdf  
14

Oct 9

Covariance

Correlation coefficients:
Pearson, Spearman

 Lecture 14

correlation_template.m

cancer_wdbc_cc_analysis_template.m

cancer_wdbc.mat

    
15

Oct 14

 Samples, histograms, 
median, quartiles, percentiles
Box-and-whisker plots		 Lecture 15 boxplot_template.m    
16

Oct 16

 Sample mean. Its mean and variance (standard error). Central limit theorem.
Parameter point estimation		 Lecture 16

central_limit_theorem_template.m

Online simulation of the Central Limit Theorem:
https://onlinestatbook.com/stat_sim/sampling_dist/

    
17

Oct 21

 Parameter point estimation. Method of moments and Maximum Likelihood Estimator.

Confidence intervals of population mean		 Lecture 17

moment_estimators_template.m

confidence_intervals_template.m

    
18

Oct 23

 Midterm review Lecture 18      
19

Oct 28

          
20 Oct 30

 

          
21

Nov 4

          
22

Nov 6

          
23

Nov 11

          
24

Nov 13

    

 

    
25

Nov 18

    

 

    
26

Nov 20

    

 

    
27

Dec 2

          
28 Dec 4          
29  Dec 9          

FINAL EXAM