Лучшие презентации на тему «Математика» на английском

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Лучшие презентации на тему «Математика» на английском

Cmpe 466 computer graphics. 3D geometric transformations. (Сhapter 9)

Cmpe 466 computer graphics. 3D geometric transformations. (Сhapter 9)

Modeling non-stationary variables

Modeling non-stationary variables

Matrices - Introduction. Lecture 1-3

Matrices - Introduction. Lecture 1-3

Hypothesis Testing with Two Samples

Hypothesis Testing with Two Samples

Probabilistic Models. Chapter 11

Probabilistic Models. Chapter 11

The mean values

The mean values

Correlation and Regression

Correlation and Regression

Geometric Modeling - Parametric Representation of Synthetic Curves

Geometric Modeling - Parametric Representation of Synthetic Curves

Confidence interval estimation

Confidence interval estimation

Discrete mathematics. Sets

Discrete mathematics. Sets

Correlation and regression

Correlation and regression

Boolean algebra. Logic operations. Formula and their conversion

Boolean algebra. Logic operations. Formula and their conversion

Forecasting with bayesian techniques MP

Forecasting with bayesian techniques MP

Confidence interval and Hypothesis testing for population mean (µ) when is known and n (large)

Confidence interval and Hypothesis testing for population mean (µ) when is known and n (large)

Solution Methods Applied Computational Fluid Dynamics. Lecture 5

Solution Methods Applied Computational Fluid Dynamics. Lecture 5

Descriptive statistics

Descriptive statistics

Statistics. Data Description. Data Summarization. Numerical Measures of the Data

Statistics. Data Description. Data Summarization. Numerical Measures of the Data

Inverse of a Square Matrix

Inverse of a Square Matrix

Descriptive statistics. Frequency distributions and their graphs. (Section 2.1)

Descriptive statistics. Frequency distributions and their graphs. (Section 2.1)

N-ary relations and their applications. (Rosen 8.2)

N-ary relations and their applications. (Rosen 8.2)

Exploring Assumptions Normality and Homogeneity of Variance

Exploring Assumptions Normality and Homogeneity of Variance

Discrete Mathematics Sets

Discrete Mathematics Sets

Combinational logic design

Combinational logic design

Exponentials and logarithms. Lecture 2.1

Exponentials and logarithms. Lecture 2.1

Normal Probability Distributions

Normal Probability Distributions

$Wide-angle X-ray scattering (WAXS) geometries. In-plane diffraction geometry$

Wide-angle X-ray scattering (WAXS) geometries. In-plane diffraction geometry

Calculating the probability of a continuous random variable – Normal Distribution. Week 9 (1)

Calculating the probability of a continuous random variable – Normal Distribution. Week 9 (1)

Hypothesis testing

Hypothesis testing

Descriptive statistics. Elementary statistics. Larson. Farber. (Chapter 2)

Descriptive statistics. Elementary statistics. Larson. Farber. (Chapter 2)

4.2 Introduction to Limits

4.2 Introduction to Limits

Discrete random variables – expected variance and standard deviation. Discrete Probability Distributions. Week 7 (1)

Discrete random variables – expected variance and standard deviation. Discrete Probability Distributions. Week 7 (1)

Random variables – discrete random variables. Week 6 (2)

Random variables – discrete random variables. Week 6 (2)

Chapter 3. Polynomial and Rational Functions. 3.4 Zeros of Polynomial Functions

Chapter 3. Polynomial and Rational Functions. 3.4 Zeros of Polynomial Functions

$Index of Refraction$

Index of Refraction

Chapter 3. Polynomial and Rational Functions. 3.2 Polynomial Functions and Their Graphs

Chapter 3. Polynomial and Rational Functions. 3.2 Polynomial Functions and Their Graphs

Binary Variables

Binary Variables

Using first derivative. Using second derivative

Using first derivative. Using second derivative

Introduction to normal distributions

Introduction to normal distributions

Sequential games. (Lecture 4)

Sequential games. (Lecture 4)

The travelling salesman problem

The travelling salesman problem

Mixed strategy Nash equilibrium. (Lecture 3)

Mixed strategy Nash equilibrium. (Lecture 3)

Modelling with exponentials and logarithms

Modelling with exponentials and logarithms

Modelling with Exponentials and Logarithms

Modelling with Exponentials and Logarithms

Theory without practice is empty, practice without theory is blind

Theory without practice is empty, practice without theory is blind

Scalars, vectors and tensors

Scalars, vectors and tensors

Math end algebra. Vector

Math end algebra. Vector

Basic arithmetic

Basic arithmetic

Measures of variation. Week 4 (2)

Measures of variation. Week 4 (2)

Stolz-Cesaro Theorem

Stolz-Cesaro Theorem

Object tracking using particle filter

Object tracking using particle filter

Evolutionary games. (Lecture 7)

Evolutionary games. (Lecture 7)

Sampling in quantitative research

Sampling in quantitative research

Discrete Probability Distributions: Binomial and Poisson Distribution. Week 7 (2)

Discrete Probability Distributions: Binomial and Poisson Distribution. Week 7 (2)

Types of Data – (continued). Week 2 (2)

Types of Data – (continued). Week 2 (2)

Сумма и разность синусов и косинусов

Сумма и разность синусов и косинусов

CENG 789 – Digital Geometry Processing

CENG 789 – Digital Geometry Processing

Probability theory. Probability Distributions Statistical Entropy

Probability theory. Probability Distributions Statistical Entropy

Matrix Equations and Systems of Linear Equations

Matrix Equations and Systems of Linear Equations

Auctions. (Lecture 10)

Auctions. (Lecture 10)

Chapter 1. Polynomial and Rational Functions. 3.3. Dividing Polynomials; Remainder and Factor Theorems

Chapter 1. Polynomial and Rational Functions. 3.3. Dividing Polynomials; Remainder and Factor Theorems

Using numerical measures to describe data. Measures of the center. Week 3 (2)

Using numerical measures to describe data. Measures of the center. Week 3 (2)

Differentiation. A “derivative”,

Differentiation. A “derivative”,

Discrete mathematics. Probability

Discrete mathematics. Probability

Measures of variation. Week 4 (1)

Measures of variation. Week 4 (1)

Recitation class

Recitation class

Economics of pricing and decision making. (Lecture 1)

Economics of pricing and decision making. (Lecture 1)

Chapter 3. Polynomial and Rational Functions. 3.1 Quadratic Functions

Chapter 3. Polynomial and Rational Functions. 3.1 Quadratic Functions

Differentiation. Sketching functions

Differentiation. Sketching functions

Simultaneous games. Oligopoly. (Lecture 2)

Simultaneous games. Oligopoly. (Lecture 2)

Ch8: Hypothesis Testing (2 Samples)

Ch8: Hypothesis Testing (2 Samples)

Method of undetermined coefficients

Method of undetermined coefficients

Propositional logic

Propositional logic

Probability Random Variables Preparatory Notes

Probability Random Variables Preparatory Notes

Sequential games. Empirical evidence and bargaining. (Lecture 5)

Sequential games. Empirical evidence and bargaining. (Lecture 5)

Matrices: Basic Operations

Matrices: Basic Operations

Arithmetic fundamentals of number systems

Arithmetic fundamentals of number systems

Types of Data – categorical data. Week 2 (1)

Types of Data – categorical data. Week 2 (1)

Correlation Regression

Correlation Regression

Introductory Statistics 1. AP Statistics

Introductory Statistics 1. AP Statistics

Random variables

Random variables

Graph transformation 1

Graph transformation 1

Foundation Year Program NUFYP Mathematics

Foundation Year Program NUFYP Mathematics

3.5 Trigonometry 5

3.5 Trigonometry 5

Expert judgment method. (Lecture 1-4)

Expert judgment method. (Lecture 1-4)

Trigonometriýa

Repeated games. (Lecture 6)

Repeated games. (Lecture 6)

Dense Linear Algebra: History and Structure, Parallel Matrix Multiplication

Dense Linear Algebra: History and Structure, Parallel Matrix Multiplication

Introduction to Statistics

Introduction to Statistics

Mathematics for Computing. Lecture 2: Logarithms and indices

Mathematics for Computing. Lecture 2: Logarithms and indices

Empirical rule - Probabilities. Week 5 (1)

Empirical rule - Probabilities. Week 5 (1)

Displaying data – shape of distributions. Week 3 (1)

Displaying data – shape of distributions. Week 3 (1)

Matlab Linear Programming

Matlab Linear Programming

Practical application of the method of Kona-Shema. Pseudopotentials method

Practical application of the method of Kona-Shema. Pseudopotentials method

Communication and signaling. (Lecture 8)

Communication and signaling. (Lecture 8)

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