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Probability - Computer Science 3

Probability - Computer Science 3


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Last Update 24/02/2022
Completion Time 3 days 6 hours 29 minutes
Members 3
Advanced Technical Computer Science
  • Probability
    41Lessons ·
    • 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
    28Lessons ·
    • 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
    107Lessons ·
    • Java Algorithms
    • Java Sort Algorithm
    • Stacks and Queues
    • Linked List in Java
    • 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 1 Introduction
    • LinkedList 2 Nodes and Size
    • LinkedList 3 Boundary Conditions
    • LinkedList 4 addFirst()
    • LinkedList 5 addLast()
    • LinkedList 6 removeFirst()
    • LinkedList 7 removeLast()
    • LinkedList 8 remove and find
    • LinkedList 9 peek()
    • LinkedList 10 Testing the list
    • LinkedList 11 Iterators
    • LinkedList 12 Double Linked Lists
    • LinkedList 13 Circular Linked Lists
    • 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 7 LoadFactor()
    • Hashes 8 Open Addressing
    • 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 2 Add Remove
    • 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 6 recursive add
    • 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 11 Radix Sort
    • 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
    1Lessons ·
    • Learn JavaScript - Full Course for Beginners
  • Linear algebra
    46Lessons ·
    • 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