Date

Topics

Slides

Matlab

Homework
(not graded)

Exams

Aug 26

 Why is knowledge of probability and statistics essential for bioengineering? Lecture 1      

Aug 28

Random experiments. Sample space, Events, Venn diagramms. 

Definitions of probability:Statistical

 Lecture 2 coin_toss_template.m    

Sep 2

Definitions of probability:
Inductive. 

Paradoxes of inductive definition of probability

Combinatorics

 Lecture 3      

Sep 4

Conditional probability

Independence of events

Bayes' theorem

Specificity/Sensitivity of tests

Circuit diagrams (start)

 Lecture 4      

Sep 9

Circuit diagrams (end)

Discrete random varibales,

PMF. CDF, CCDF, 
Mean, Variance, Skewness, Geometric mean

 Lecture 5 circuit_template.m    

Sep 11

Uniform distribution

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

Bernoulli trials

Binomial Distribution

 Lecture 6 binomial_template.m  


 

Sep 16

 Poisson Distribution

Genome Assembly 		 Lecture 7

 

    

Sep 18

Phylogenetic trees and time to the most recent common ancestor. 

Mitochondrial Eve and Y-chromosme Adam. 

Time to the Most Recent Common Ancestor (MRCA) for the nuclear genome.

Negative Binomial Distribution

Cancer: Driver and Passenger Genes

 

 Lecture 8      

Sep 23

Secretary problem

Monty Hall problem

no in-person class:
watch the recorded lecture

Lecture 9

Video recording

 

    

Sep 25

Midterm review

no in-person class:
review the slides

 Midterm 1 reveiw

 

    

Sep 30

 

no in-person class:
exam time


Midterm exam 1 will be held 
Tue Sep 29 – Thu Oct 1
Duration 50 min,
Self-reserve opens Thu Sep 17

  

 

    

Oct 2

Continuous random variables.

Probability Density Function, CDF, CCDF, Mean, Variance, Standard deviation. Skewness.

Uniform continuous distribution. 

Constant rate (Poisson) process.

Exponential distribution.

 Lecture 10      

Oct 7

Survivorship curves and age-adjusted death rates

Erland and Gamma distributions

Gaussian distribution

Standardizing and working with the CDF table

 Lecture 11 exponential_gamma_template.m    

Oct 9

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 12

PINT_binding_energy.m

PINT_binding_energy.mat

    

Oct 14

Multiple continuous random variables

Covariance,
Correlation coefficients:
Pearson, Spearman

 Lecture 13

correlation_template.m

cancer_wdbc_cc_analysis_template.m

cancer_wdbc.mat

    

Oct 16

Samples, histograms, 
median, quartiles, percentiles
Box-and-whisker plots

Sample mean. Its mean and variance (standard error). Central limit theorem.

 Lecture 14

boxplot_template.m

    

Oct 21

 Central limit theorem (continued)

Sample mean - an unbiased estimator of population mean.

Sample variance - an unbiased estimator of population variants.

Point estimation of distribution paramaters.

Method of moments.

 Lecture 15

https://onlinestatbook.com/stat_sim/sampling_dist/

central_limit_theorem.m

    

Oct 23

Maximum Likelihood Estimator.

Confidence intervals of population mean

Student-t distribution

 Lecture 16

confidence_intervals_template.m

http://demonstrations.wolfram.com/ComparingNormalAndStudentsTDistributions/

https://demonstrations.wolfram.com/ChiSquaredDistributionAndTheCentralLimitTheorem/

 

    

Oct 28

          

Oct 30

          

Nov 4

 Tentative dates of the
midterm exam 2 are 
Tue Nov 3 – Thu Nov 5
Duration 50 min,
Self-reserve opens Thu Oct 22

In case the exam will be held on these dates,  this lecture will be canceled		        

Nov 6

          

Nov 11

          

Nov 13

    

 

    

Nov 18

    

 

    

Nov 20

    

 

    

Dec 2

          
Dec 4          
Dec 10-
Dec 15		 Tentative dates of the
final exam are 
Thu Dec 10 – Tue Dec 15
Duration: 1 h 50 min
Registration opens: Mon Oct 19