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# |
Date |
Topics |
Slides |
Matlab |
Homework |
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: |
Lecture 2 | |||
| 3 |
Sep 2 |
Definitions of probability: Paradoxes of inductive definition of probability Combinatorics |
Lecture 3 | |||
| 4 |
Sep 4 |
Combinatorics (continued) Conditional probability 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, Uniform distribution |
Lecture 6 | uniform_discrete_template.m | hw1.pdf |
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| 7 |
Sep 16 |
Bernoulli trials Binomial Distribution Poisson Distribution |
Lecture 7 | |||
| 8 |
Sep 18 |
Poisson distribution in genome assembly | Lecture 8 | poisson_template.m | ||
| 9 |
Sep 23 |
Geometric distribution. Mitochondrial Eve & |
Lecture 9 |
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| 10 |
Sep 25 |
Negative Binomial Distribution Cancer: Driver and Passenger genes |
Lecture 10 | 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 | |||
| 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 | hw2_with_solutions.pdf | ||
| 14 |
Oct 9 |
Covariance Correlation coefficients: |
Lecture 14 | |||
| 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: |
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| 17 |
Oct 21 |
Parameter point estimation. Method of moments and Maximum Likelihood Estimator. Confidence intervals of population mean |
Lecture 17 | |||
| 18 |
Oct 23 |
Midterm review | Lecture 18 | |||
| 19 |
Oct 28 |
Midterm exam at CBTF | ||||
| 20 | Oct 30
|
Midterm exam at CBTF | ||||
| 21 |
Nov 4 |
Confidence intervals of population mean and variance Student-t and chi-squared distributions Confidence interval of population proportion |
Lecture 19 | |||
| 22 |
Nov 6 |
Hypothesis testing: Type 1 and Type 2 error. One- and two-sided hyptheses. One and two samples. |
Lecture 20 | dark_vs_milk_chocolate_analysis_template.m | ||
| 23 |
Nov 11 |
In-class group exercise #1 | ||||
| 24 |
Nov 13 |
In-class group exercise #2 | ||||
| 25 |
Nov 18 |
Pearson's chi-square Goodness of Fit (GOF) test | Lecture 21 | |||
| 26 |
Nov 20 |
Linear regression: two variables. Nonlinear regression. Training and testing/validation sets. Overfitting. Double descent |
Lecture 22 |
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| 27 |
Dec 2 |
Matlab exercise on single and multiple variable regression. Clustering Analysis |
Lecture 23 | |||
| 28 | Dec 4 |
Clustering Matlab exrcise. Using Gephi software for network analysis and visualization. |
Lecture 24 |
coexpression_network_random start.gephi |
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| 29 | Dec 9 | |||||
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FINAL EXAM |