# |
Date |
Topics |
Slides |
Matlab |
Homework |
1 |
Jan 16 |
Class logistics
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Lecture 1 | ||
2 |
Jan 18 |
Random experiments.
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Lecture 2 | ||
3 |
Jan 23 |
Definitions of probability: inductive or logical Combinatorics Birthday problem |
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4 |
Jan 25 |
Combinatorics: Polya Urn problem Axioms of probability Conditional probability Event independence |
Lecture 4 | ||
5 |
Jan 30 |
Circuit problems Bayes theorem. |
Lecture 5 | ||
6 |
Feb 1 |
Simpson's paradox Monty Hall problem |
Lecture 6 | ||
7 |
Feb 6 |
Random variables: discrete vs continuous PMF, CDF, CCDF, mean, variance, standard deviation, skewness, geometric mean Uniformly distributed random variables |
Lecture 7 |
|
hw1.pdf |
8 |
Feb 8 |
Bernoulli trial Binomial distribution Matlab exercises |
Lecture 8 |
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9 |
Feb 13 |
Poisson Distribution Genome Assembly. Applications of the Poisson distribution |
Lecture 9 |
|
hw1 solutions |
10 |
Feb 15 |
Geometric Distribution. Mitochondrial Eve and |
Lecture 10 |
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|
11 |
Feb 20 |
MRCA in nuclear part of the genome Negative binomial distribution. Driver and passesnger mutations in cancer |
Lecture 11 |
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12 |
Feb 22 |
Continuous random variables: PDF Continuous uniform distribution |
Lecture 12 |
|
hw2.pdf |
13 |
Feb 27 |
Gaussian or Normal Distribution. Standardizing random variables. |
Lecture 13 | exponential_gamma_template.m | |
14 |
Feb 29 |
Erlang distribution | Lecture 14 | ||
15 |
Mar 5 |
Protein Protein Interations (PPIs) Lognormal Distribution Two random variables Joint Distributions Marginal, conditional probabilities |
Lecture 15 | PINT_binding_energy.mat | |
16 |
Mar 7 |
Continuous Joint Distributions Covariance and Correlation |
Lecture 16 | correlation_template.m | hw2_solutions |
17 |
Mar 19 |
Descriptive statistics Samples of i.i.d. random variables Median, quartiles, percentiles |
Lecture 17 | ||
18 |
Mar 21 |
Misterm review | Lecture 18 | ||
Mar 26 |
MIDTERM during regular lecture hours | midterm_with_solutions.pdf | |||
19 |
Mar 28 |
Boxplot, vase and violin diagrams Probability plots: light- and heavy-tailed distributions Sample statistic:Sample mean, its average value and standard deviation. Sampling distribution of the sample mean: Central Limit Theorem. |
Lecture 19 | ||
20 |
Apr 2 |
Parameter point estimation Sample variance Method of Moments |
Lecture 20 | moment_estimators_template.m | |
21 |
Apr 4 |
Method of Maximum Likelihood Estimation Confidence intervals. T-statistic and Student's T-distribution |
Lecture 21 | hw3.pdf | |
22 |
Apr 9 |
Chi-squared distribution Hypothesis testing : Type I and Type-II errors |
Lecture 22 | Chi-squared distribution | |
23 |
Apr 11 |
Hypothesis testing with two samples Bonferroni correction for multiple hypotheses Chi-squared goodness of fit test with k classes. Test of statistical independence.
|
Lecture 23 | hw3_solutions | |
24 |
Apr 16 |
Two variable linear regression | Lecture 24 |
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25 |
Apr 18 |
How to avoid overfitting the data. Training and testing datasets Double descent Multiple linear regression Adjusted R-squared |
Lecture 25 | hw4.pdf | |
26 |
Apr 23 |
Clustering data | Lecture 26 | ||
27 |
Apr 25 |
Topics in network analysis: Co-expression networks Visualization of large networks using Gephi |
Lecture 27 |
gephi_network_analysis_exercise.pdf |
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28 | Apr 30 | Final exam reviw | hw4_solutions | ||
FINAL EXAM |