Math and Probability for Life Sciences

Course Description

This is a math course aimed at students with life science majors covering elementary probability, probability distributions, random variables, and limit theorems.

Lectures

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   Lecture 1 - Introduction: Probability and Counting

   Outcomes, sample, space, events, and probability functions.

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   Lecture 2 - Probability Functions

   Probability functions, permuations.

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   Lecture 3 - Permutations

   Permutations.

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   Lecture 4 - Probability Functions (continued)

   Probability functions.

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   Lecture 5 - Conditional Probability

   Conditional probability.

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   Lecture 6 - Conditional Probability (continued)

   Conditional probability.

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   Lecture 7 - Independent Events

   independence of 3 or more events.

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   Lecture 8 - Random Variables

   Random variables.

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   Lecture 9 - Expected Values

   Random variables (continued), expected value, standard deviations.

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    Lecture 10 - Binomial Distributions

    Standard distribution, binomial distribution, two random variables.

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    Lecture 11 - Midterm Review

    Midterm review.

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    Lecture 12 - Multinomial Distributions

    Multivariable distribution, binomial distribution, Bernoulli trials, geometric distributions.

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    Lecture 13 - Geometric Distributions

    Geometric distributions.

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    Lecture 14 - Poisson Distributions

    Poisson distribution, continuous trials, poisson processes.

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    Lecture 15 - Poisson Distributions (continued)

    Poisson distribution, poisson processes.

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    Lecture 16 - Density Function

    Density funtion, continuous random variables, uniform distribution.

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    Lecture 17 - Exponential Distributions

    Continuous random variables, exponential distribution.

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    Lecture 18 - Normal Distributions

    Normal distribution.

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    Lecture 19 - Normal Distributions (continued)

    Standard normal distribution, cumulative distribution function.

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    Lecture 20 - Standard Normal Distributions

    Nomal distribution continued.

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    Lecture 21 - Central Limit Theorem

    Central limit theorem, normal distribution applications.

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    Lecture 22 - Hitstogram Correction

    Normal approximation, histogram correction.

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    Lecture 23 - Midterm Review 2

    Midterm review 2.

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    Lecture 24 - Analyzing Data in Probability

    Analyzing data in probability, samples, incomplete data, means.

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    Lecture 25 - Analyzing Data in Probability (continued)

    Analyzing data in probability, samples, incomplete data, means.

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    Lecture 26 - Limit Theorems

    Limit theorems, Markovs inequality theorem, Chebyshevs inequality theorem, Law of large numbers.

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    Lecture 27 - Limit Theorems (continued)

    Limit theorems, Markovs inequality theorem, Chebyshevs inequality theorem, Law of large numbers.

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    Lecture 28 - Course Review

    Course Review