 # Probability - Computer Science 3

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• Probability
• Probability explained | Independent and dependent events | Probability and Statistics | Khan Academy
• Probability with playing cards and Venn diagrams | Probability and Statistics | Khan Academy
• Addition rule for probability | Probability and Statistics | Khan Academy
• Finding probability example 2 | Probability and Statistics | Khan Academy
• Compound probability of independent events | Probability and Statistics | Khan Academy
• Coin flipping probability | Probability and Statistics | Khan Academy
• Probability without equally likely events | Probability and Statistics | Khan Academy
• Getting exactly two heads (combinatorics) | Probability and Statistics | Khan Academy
• Exactly three heads in five flips | Probability and Statistics | Khan Academy
• Frequency stability property short film | Computer Science | Khan Academy
• Generalizing with binomial coefficients (bit advanced) | Probability and Statistics | Khan Academy
• Probability (part 2)
• Probability (part 3)
• Probability (part 4)
• Probability (part 5)
• Probability (part 6)
• Dependent probability example | Probability and Statistics | Khan Academy
• Dependent probability example 2 | Probability and Statistics | Khan Academy
• Probability (part 7)
• Probability (part 8)
• Permutations
• Combinations
• Probability using combinations | Probability and Statistics | Khan Academy
• Probability and combinations (part 2) | Probability and Statistics | Khan Academy
• Conditional probability and combinations | Probability and Statistics | Khan Academy
• Birthday probability problem | Probability and Statistics | Khan Academy
• Introduction to Random Variables
• Probability density functions | Probability and Statistics | Khan Academy
• Binomial Distribution 1
• Binomial Distribution 2
• Binomial Distribution 3
• Binomial Distribution 4
• Expected Value: E(X)
• Expected value of binomial distribution | Probability and Statistics | Khan Academy
• Poisson process 1 | Probability and Statistics | Khan Academy
• Poisson process 2 | Probability and Statistics | Khan Academy
• Law of large numbers | Probability and Statistics | Khan Academy
• Term life insurance and death probability | Finance & Capital Markets | Khan Academy
• Mega millions jackpot probability | Probability and combinatorics | Precalculus | Khan Academy
• Free throwing probability | Probability and Statistics | Khan Academy
• Three pointer vs free throwing probability | Probability and Statistics | Khan Academy
• Statistics
• Statistics Lecture 1.1: The Key Words and Definitions For Elementary Statistics
• Statistics Lecture 1.3: Exploring Categories of Data, Levels of Measurement
• Statistics Lecture 1.5: Sampling Techniques. How to Develop a Random Sample
• Statistics Lecture 2.2: Creating Frequency Distribution and Histograms
• Statistics Lecture 3.2: Finding the Center of a Data Set. Mean, Median, Mode
• Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
• Statistics Lecture 3.4: Finding Z-Score, Percentiles and Quartiles, and Comparing Standard Deviation
• Statistics Lecture 4.2: Introduction to Probability
• Statistics Lecture 4.3: The Addition Rule for Probability
• Statistics Lecture 4.4: The Multiplication Rule for "And" Probabilities.
• Statistics Lecture 4.5: Probability of Complementary Events with "At Least One"
• Statistics Lecture 4.7: Fundamental Counting Rule, Permutations and Combinations
• Statistics Lecture 5.2: A Study of Probability Distributions, Mean, and Standard Deviation
• Statistics Lecture 5.3: A Study of Binomial Probability Distributions
• Statistics Lecture 5.4: Finding Mean and Standard Deviation of a Binomial Probability Distribution
• Statistics Lecture 6.2: Introduction to the Normal Distribution and Continuous Random Variables
• Statistics Lecture 6.3: The Standard Normal Distribution. Using z-score, Standard Score
• Statistics Lecture 6.4: Sampling Distributions Statistics. Using Samples to Approx. Populations
• Statistics Lecture 6.5: The Central Limit Theorem for Statistics. Using z-score, Standard Score
• Statistics Lecture 7.2: Finding Confidence Intervals for the Population Proportion
• Statistics Lecture 7.3: Confidence Interval for the Sample Mean, Population Std Dev -- Known
• Statistics Lecture 7.4: Confidence Interval for the Sample Mean, Population Std Dev -- Unknown
• Statistics Lecture 7.5: Confidence Intervals for Variance and Std Dev. Chi-Squared Distribution.
• Statistics Lecture 8.2: An Introduction to Hypothesis Testing
• Statistics Lecture 8.3: Hypothesis Testing for Population Proportion
• Statistics Lecture 8.4: Hypothesis Testing for Population Mean. Population Std Dev is Known.
• Statistics Lecture 8.5: Hypothesis Testing for Population Mean. Population Std Dev is Unknown.
• Statistics Lecture 8.6: Hypothesis Testing Involving Variance and Standard Deviation.
• Algorithms and data structures
• Java Algorithms
• Java Sort Algorithm
• Stacks and Queues
• Linked List in Java 2
• Java Recursion
• Java Shell Sort
• Java Quick Sort
• Big O Notations
• Java Hash Table
• Java Hash Tables 2
• Java Hash Tables 3
• Java Binary Search Tree
• Java Binary Search Tree 2
• Solving Programming Problems
• Solving Programming Problems 2
• Java Heaps
• Welcome to Data Structures
• Complexity 1 Introduction to complexity
• Complexity 2 Big Oh Notation
• Complexity 3 Some examples of big-Oh notation
• Java 1 ObjectOrientedProgramming
• Java 2 ComparableGenerics
• Java 3 Introduction to Generic Programming
• Java 4 Parameterized Types
• Java 5 Autoboxing
• Java 6 Exceptions
• LinkedList 2 Nodes and Size
• LinkedList 8 remove and find
• LinkedList 10 Testing the list
• Stacks and Queues 3 Using arrays to write stacks and queues
• Hashes 1 Introduction
• Hashes 2 Hash Functions
• Hashes 3 Collisions
• Hashes 4 Hash Functions for Strings
• Hashes 5 Compressing numbers to fit the size of the array
• Hashes 6 Make an integer positive
• Hashes 9 Chaining
• Hashes 10 Rehashing
• Hashes 11 the hash class
• Hashes 12 Review of the hash element inner class
• Hashes 13 Constructor for a chained hash.
• Hashes 14 Review of constructors
• Hashes 15 add() and remove() methods
• Hashes 16 getValue()
• Hashes 17 resize
• Hashes 18 KeyIterator
• Trees and heaps 1 Introduction
• Heaps 1 Introduction and Tree levels
• Heaps 3 TrickleUp
• Heaps 4 TrickleDown
• Heaps 5 HeapSort
• Trees 2 Complete and Full
• Trees 3 Traversal
• Trees 4 Expression Trees
• Trees 5 Node Class
• Trees 7 Contains
• Trees 8 Remove
• Trees 9 Introduction to rotations
• Trees 10 Rotations
• Trees 11 Coding Rotations
• AVL 1 Introduction
• AVL Tree 2 Nodes
• AVL Tree 3 Adding a node
• AVL Tree 4 recursive add for an AVL tree
• AVL Tree 5 checking balance in an AVL tree
• AVL Tree 6 Rebalancing AVL trees
• AVL Tree 7 complete example of adding data to an AVL tree.
• Red Black Tree 1 The Rules
• Red Black Trees 2 Example of building a tree
• Red Black Tree 3 - Classes
• Red Black Tree 4 - Add methods
• Red Black Tree 5 checking violations in the tree
• Red Black Tree 6 The Rotate method
• Red Black Tree 7 left rotate
• Red Black Tree 8 leftRightRotate
• Red Black Tree 9 height
• Red Black Tree 10 number of black nodes
• Sorts 1 Introduction to sorts
• Sorts 2 Selection Sort
• Sorts 3 Insertion Sort
• Sorts 4 Insertion Sort Code
• Sorts 5 Shell Sort
• Sorts 6 Merge Sort
• Sorts 7 Merge Sort Code
• Sorts 8 Quick Sort
• Sorts 9 Quick Sort Worst Case
• Sorts 10 Quick Sort Code
• Sorts 12 Sort Summary
• Bloom Filters
• k-mer algorithms: Compare and Swap
• Data Structures Easy to Advanced Course - Full Tutorial from a Google Engineer
• Intro to client-side development
• Learn JavaScript - Full Course for Beginners
• Linear algebra
• Linear Algebra 1.1.1 Systems of Linear Equations
• Linear Algebra 1.1.2 Solve Systems of Linear Equations in Augmented Matrices Using Row Operations
• Linear Algebra 1.2.1 Row Reduction and Echelon Forms
• Linear Algebra 1.2.2 Solution Sets and Free Variables
• Linear Algebra 1.3.1 Vector Equations
• Linear Algebra 1.3.2 Linear Combinations
• Linear Algebra 1.4.1 The Matrix Equation Ax=b
• Linear Algebra 1.4.2 Computation of Ax
• Linear Algebra 1.5.1 Homogeneous System Solutions
• Linear Algebra 1.5.2 Non-Homogeneous System Solutions
• Linear Algebra 1.6.1 Applications of Linear Systems - Economic Sectors
• Linear Algebra 1.6.2 Applications of Linear Systems - Network Flow
• Linear Algebra 1.7.1 Linear Independence
• Linear Algebra 1.7.2 Special Ways to Determine Linear Independence
• Linear Algebra 1.8.1 Matrix Transformations
• Linear Algebra 1.8.2 Introduction to Linear Transformations
• Linear Algebra 2.1.1 Matrix Operations - Sums and Scalar Multiples
• Linear Algebra 2.1.2 Matrix Operations - Multiplication and Transpose
• Linear Algebra 2.2.1 The Inverse of a Matrix
• Linear Algebra 2.2.2 Solving 2x2 Systems with the Inverse and Inverse Properties
• Linear Algebra 2.2.3 Elementary Matrices And An Algorithm for Finding A Inverse
• Linear Algebra 2.3.1 Characterizations of Invertible Matrices
• Linear Algebra 3.1.1 Introduction to Determinants
• Linear Algebra 3.1.2 Co-factor Expansion
• Linear Algebra 3.2.1 Properties of Determinants
• Linear Algebra 4.1.1 Vector Spaces
• Linear Algebra 4.1.2 Subspace of a Vector Space
• Linear Algebra 4.2.1 Null Spaces
• Linear Algebra 4.2.2 Column Spaces
• Linear Algebra 4.3.1 Linearly Independent Sets and Bases
• Linear Algebra 4.3.2 The Spanning Set Theorem
• Linear Algebra 4.5.1 The Dimension of a Vector Space
• Linear Algebra 4.5.2 Subspaces of a Finite Dimensional Space
• Linear Algebra 4.6.1 The Row Space
• Linear Algebra 4.6.2 Rank
• Linear Algebra 5.1.1 Eigenvectors and Eigenvalues
• Linear Algebra 5.1.2 More About Eigenvectors and Eigenvalues
• Linear Algebra 5.2.1 Determinants and the IMT
• Linear Algebra 5.2.2 The Characteristic Equation
• Linear Algebra 6.1.1 Inner Product, Vector Length and Distance
• Linear Algebra 6.1.2 Orthogonal Vectors
• Linear Algebra 6.2.1 Orthogonal Sets
• Linear Algebra 6.2.2 Orthogonal Projections
• Linear Algebra 6.3.1 Orthogonal Decomposition Theorem
• Linear Algebra 6.3.2 The Best Approximation Theorem
• Linear Algebra 6.5.1 Least Squares Problems