# |
Date |
Topics |
Slides |
Matlab |
Homework |
Videos |
1 |
Aug 22 |
Class logistics. Two reasons we need probability and statistics in computational bioengineering. |
Lecture_1 | |||
2 |
Aug 24 |
Random experiments. |
Lecture_2 | |||
3 |
Aug 29 |
Definitions of probability: inductive or logical. |
Lecture_3 | |||
4 |
Aug 31 |
Axioms of probability. |
Lectire_4 | circuit_template.m | ||
5 |
Sep 5 |
Circuits (continued) Bayes theorem. Secretary problem. Simpson's paradox |
Lecture_5 | |||
6 |
Sep 7 |
Simpson's paradox Monty Hall problem Discrete random variables: |
Lecture_6 | hw1.pdf |
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7 |
Sep 12 |
Random variables: skewness, geometric mean Discrete distributions: Uniform The distribution of Ct values of a viral PCR test |
Lecture_7 | |||
8 |
Sep 14 |
Binomial distribution Poisson distribution Genome assembly (start) |
Lecture_8 | |||
9 |
Sep 19 |
Genome assembly (continued) Geometric distribution |
Lecture_9 | |||
10 |
Sep 21 |
Mitochondrial Eve Negative Binomial Distribution Cancer drivers and passengers |
Lecture_10 | |||
11 |
Sep 26 |
Review of discrete distributions. Continuous random variables: PDF, CDF, CCDF, mean, variance. Constant rate process. Exponential distribution |
Lecture_11 |
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12 |
Sep 28 |
Constant rate process (continued) Memoryless property of the exponential distribution Erlang and Gamma distributions |
Lecture_12 | hw2_with_solutions.pdf | ||
13 |
Oct 3 |
Work in class on Group_Project_1 |
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14 |
Oct 5 |
Gaussian distribution Standardization |
Work in class on Group_Project_2
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15 |
Oct 10 |
Multiple variables: Statistical independence of variables Covariation Correlation coefficients: |
Work in class on Group_Project_3 Joint, Marginal, Conditional probablities, Statistical indepndence of variables
|
cancer_wdbc.mat | ||
16 |
Oct 12 |
Linear Functions of Random Variables Principal Component Ananlysis |
Work in class on Videos relevant for this project: |
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17 |
Oct 17 |
Descriptive statistics. Samples, i.i.d. random variables, Histograms. |
Lecture_17 | |||
18 |
Oct 19 |
Central Limit Theorem Parameter estimators: |
Lecture_18 | hw3.pdf | ||
19 |
Oct 24 |
Sample variance S^2 (unbiased estimator) Maximum Likelihood Estimator (MLE) Confidence Intervals - with known population variance, sigma^2 |
Lecture_19 | |||
20 |
Oct 26 |
Confidence Intervals - for population average with estimated population variance via sample variance, S^2. Student T-distribution - for population variance. Chi-squared distribution. - for population fraction |
Lecture_20 | hw3_with_solutions.pdf | ||
21 |
Oct 31 |
Hypothesis tested (one- and two-sided). One sample and two samples hypotheses Bonferroni correction for multiple hypotheses |
Lecture_21 | dark_vs_milk_chocolate_analysis_template.m | ||
22 |
Nov 2 |
Midterm review | Lecture_22 | |||
Nov 7 |
MIDTERM | |||||
23 |
Nov 9 |
Linear regression (single variable) |
Lecture_23 |
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24 |
Nov 14 |
Chi-squared Goodness of Fit Test. M&M colors exercise | Lecture_24 | hw4.pdf | ||
25 |
Nov 16 |
Multiple Linear Regression. adjusted R-squared Use of testing and training samples to avoid overfitting. |
Lecture_25 | |||
26 |
Nov 28 |
Supervised and unsupervised machine learning. Clustering |
Lecture_26 | clustering_template.m | hw4_with_solutions.pdf | |
27 | Nov 30 |
Gene Set Enrichment Analysis (GSE) of biological functions in gene expression clusters using NCI David Netwoek analysis: hubs, PageRank, betweenness-centrality Network visualization using Gephi |
Lecture_27 |
coexpression_network_random start.gephi |
hw5.pdf | |
28 |
Dec 5 | Final exam review | Lecture_28 | hw5_with_solutions.pdf | ||
FINAL EXAM |