Master Complete Statistics For Computer Science – I
Course In Probability & Statistics Important For Machine Learning, Artificial Intelligence, Data Science, Neural Network
What you’ll learn
 Random Variables
 Discrete Random Variables and its Probability Mass Function
 Continuous Random Variables and its Probability Density Function
 Cumulative Distribution Function and its properties and application
 Special Distribution
 Two – Dimensional Random Variables
 Marginal Probability Distribution
 Conditional Probability Distribution
 Independent Random Variables
 Function of One Random Variable
 One Function of Two Random Variables
 Two Functions of Two Random Variables
 Statistical Averages
 Measures of Central Tendency (Mean, Median, Mode, Geometric Mean and Harmonic Mean)
 Mathematical Expectations and Moments
 Measures of Dispersion (Quartile Deviation, Mean Deviation, Standard Deviation and Variance)
 Skewness and Kurtosis
 Expected Values of TwoDimensional Random Variables
 Linear Correlation
 Correlation Coefficient and its properties
 Rank Correlation Coefficient
 Linear Regression
 Equations of the Lines of Regression
 Standard Error of Estimate of Y on X and of X on Y
 Characteristic Function and Moment Generating Function
 Bounds on Probabilities
Requirements
 Knowledge of Applied Probability
 Knowledge of Calculus
Description
In todays engineering curriculum, topics on probability and statistics play a major role, as the statistical methods are very helpful in analyzing the data and interpreting the results.
When an aspiring engineering student takes up a project or research work, statistical methods become very handy.
Hence, the use of a wellstructured course on probability and statistics in the curriculum will help students understand the concept in depth, in addition to preparing for examinations such as for regular courses or entrylevel exams for postgraduate courses.
In order to cater the needs of the engineering students, content of this course, are well designed. In this course, all the sections are well organized and presented in an order as the contents progress from basics to higher level of statistics.
As a result, this course is, in fact, student friendly, as I have tried to explain all the concepts with suitable examples before solving problems.
This 150+ lecture course includes video explanations of everything from Random Variables, Probability Distribution, Statistical Averages, Correlation, Regression, Characteristic Function, Moment Generating Function and Bounds on Probability, and it includes more than 90+ examples (with detailed solutions) to help you test your understanding along the way. “Master Complete Statistics For Computer Science – I” is organized into the following sections:

Introduction

Discrete Random Variables

Continuous Random Variables

Cumulative Distribution Function

Special Distribution

Two – Dimensional Random Variables

Random Vectors

Function of One Random Variable

One Function of Two Random Variables

Two Functions of Two Random Variables

Measures of Central Tendency

Mathematical Expectations and Moments

Measures of Dispersion

Skewness and Kurtosis

Statistical Averages – Solved Examples

Expected Values of a TwoDimensional Random Variables

Linear Correlation

Correlation Coefficient

Properties of Correlation Coefficient

Rank Correlation Coefficient

Linear Regression

Equations of the Lines of Regression

Standard Error of Estimate of Y on X and of X on Y

Characteristic Function and Moment Generating Function

Bounds on Probabilities
Author(s): Shilank Singh