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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
Hypothesis Testing with Two Samples
Hypothesis Testing with Two Samples
The mean values
The mean values
Probabilistic Models. Chapter 11
Probabilistic Models. Chapter 11
Correlation and Regression
Correlation and Regression
Correlation and regression
Correlation and regression
Forecasting with bayesian techniques MP
Forecasting with bayesian techniques MP
Discrete mathematics. Sets
Discrete mathematics. Sets
Numbers 11-20
Numbers 11-20
Confidence interval estimation
Confidence interval estimation
Discrete Mathematics Sets
Discrete Mathematics Sets
Boolean algebra. Logic operations. Formula and their conversion
Boolean algebra. Logic operations. Formula and their conversion
Geometric Modeling - Parametric Representation of Synthetic Curves
Geometric Modeling - Parametric Representation of Synthetic Curves
N-ary relations and their applications. (Rosen 8.2)
N-ary relations and their applications. (Rosen 8.2)
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)
Statistics. Data Description. Data Summarization. Numerical Measures of the Data
Statistics. Data Description. Data Summarization. Numerical Measures of the Data
Matrices - Introduction. Lecture 1-3
Matrices - Introduction. Lecture 1-3
Descriptive statistics
Descriptive statistics
Combinational logic design
Combinational logic design
Hypothesis testing
Hypothesis testing
Wide-angle X-ray scattering (WAXS) geometries. In-plane diffraction geometry
Wide-angle X-ray scattering (WAXS) geometries. In-plane diffraction geometry
Descriptive statistics. Frequency distributions and their graphs. (Section 2.1)
Descriptive statistics. Frequency distributions and their graphs. (Section 2.1)
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)
Exploring Assumptions Normality and Homogeneity of Variance
Exploring Assumptions Normality and Homogeneity of Variance
Index of Refraction
Index of Refraction
Exponentials and logarithms. Lecture 2.1
Exponentials and logarithms. Lecture 2.1
Sequential games. (Lecture 4)
Sequential games. (Lecture 4)
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)
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
Random variables – discrete random variables. Week 6 (2)
Random variables – discrete random variables. Week 6 (2)
Theory without practice is empty, practice without theory is blind
Theory without practice is empty, practice without theory is blind
Descriptive statistics. Elementary statistics. Larson. Farber. (Chapter 2)
Descriptive statistics. Elementary statistics. Larson. Farber. (Chapter 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
Solution Methods Applied Computational Fluid Dynamics. Lecture 5
Solution Methods Applied Computational Fluid Dynamics. Lecture 5
Evolutionary games. (Lecture 7)
Evolutionary games. (Lecture 7)
Inverse of a Square Matrix
Inverse of a Square Matrix
Normal Probability Distributions
Normal Probability Distributions
Mixed strategy Nash equilibrium. (Lecture 3)
Mixed strategy Nash equilibrium. (Lecture 3)
Modelling with Exponentials and Logarithms
Modelling with Exponentials and Logarithms
Determinants
Determinants
The travelling salesman problem
The travelling salesman problem
Recitation class
Recitation class
Using first derivative. Using second derivative
Using first derivative. Using second derivative
Statistical data processing
Statistical data processing
CENG 789 – Digital Geometry Processing
CENG 789 – Digital Geometry Processing
Object tracking using particle filter
Object tracking using particle filter
Auctions. (Lecture 10)
Auctions. (Lecture 10)
Economics of pricing and decision making. (Lecture 1)
Economics of pricing and decision making. (Lecture 1)
Types of Data – (continued). Week 2 (2)
Types of Data – (continued). Week 2 (2)
Chapter 3. Polynomial and Rational Functions. 3.1 Quadratic Functions
Chapter 3. Polynomial and Rational Functions. 3.1 Quadratic Functions
Measures of variation. Week 4 (2)
Measures of variation. Week 4 (2)
4.2 Introduction to Limits
4.2 Introduction to Limits
Introduction to normal distributions
Introduction to normal distributions
Probability theory. Probability Distributions Statistical Entropy
Probability theory. Probability Distributions Statistical Entropy
Discrete Probability Distributions: Binomial and Poisson Distribution. Week 7 (2)
Discrete Probability Distributions: Binomial and Poisson Distribution. Week 7 (2)
Basic arithmetic
Basic arithmetic
Measures of variation. Week 4 (1)
Measures of variation. Week 4 (1)
Discrete mathematics. Probability
Discrete mathematics. Probability
Binary Variables
Binary Variables
Sequential games. Empirical evidence and bargaining. (Lecture 5)
Sequential games. Empirical evidence and bargaining. (Lecture 5)
Simultaneous games. Oligopoly. (Lecture 2)
Simultaneous games. Oligopoly. (Lecture 2)
Repeated games. (Lecture 6)
Repeated games. (Lecture 6)
Stolz-Cesaro Theorem
Stolz-Cesaro Theorem
Matrix Equations and Systems of Linear Equations
Matrix Equations and Systems of Linear Equations
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”,
Trigonometriýa
Trigonometriýa
Scalars, vectors and tensors
Scalars, vectors and tensors
Mechanism design. (Lecture 9)
Mechanism design. (Lecture 9)
Empirical rule - Probabilities. Week 5 (1)
Empirical rule - Probabilities. Week 5 (1)
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
Introductory Statistics 1. AP Statistics
Introductory Statistics 1. AP Statistics
Modelling with exponentials and logarithms
Modelling with exponentials and logarithms
Differentiation. Sketching functions
Differentiation. Sketching functions
Communication and signaling. (Lecture 8)
Communication and signaling. (Lecture 8)
Introduction to Statistics
Introduction to Statistics
Types of Data – categorical data. Week 2 (1)
Types of Data – categorical data. Week 2 (1)
Displaying data – shape of distributions. Week 3 (1)
Displaying data – shape of distributions. Week 3 (1)
Probability Random Variables Preparatory Notes
Probability Random Variables Preparatory Notes
3.5 Trigonometry 5
3.5 Trigonometry 5
Elements of probability. (Lecture 3)
Elements of probability. (Lecture 3)
Time quiz
Time quiz
Random variables
Random variables
Introduction to Statistics. Week 1 (1)
Introduction to Statistics. Week 1 (1)
Probabilities. Week 5 (2)
Probabilities. Week 5 (2)
Matlab Linear Programming
Matlab Linear Programming
Vectors and the geometry of space
Vectors and the geometry of space
Ch8: Hypothesis Testing (2 Samples)
Ch8: Hypothesis Testing (2 Samples)
Dense Linear Algebra: History and Structure, Parallel Matrix Multiplication
Dense Linear Algebra: History and Structure, Parallel Matrix Multiplication
Basics of functions and their graphs
Basics of functions and their graphs
Mathematics for Computing 2016-2017. Lecture 1: Course Introduction and Numerical Representation
Mathematics for Computing 2016-2017. Lecture 1: Course Introduction and Numerical Representation
Correlation Regression
Correlation Regression
Mathematics for Computing. Lecture 2: Logarithms and indices
Mathematics for Computing. Lecture 2: Logarithms and indices
The Gilbert-Johnson-Keerthi (GJK) Algorithm
The Gilbert-Johnson-Keerthi (GJK) Algorithm
Probability Concepts
Probability Concepts
Graph transformation 1
Graph transformation 1
Math end algebra. Vector
Math end algebra. Vector
Arithmetic fundamentals of number systems
Arithmetic fundamentals of number systems
Evolutionary game theory. (Lecture 11)
Evolutionary game theory. (Lecture 11)
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