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Computer Science
Computer Science Fundamentals
Mathematical Foundations for Computing
1. Foundations of Logic and Proofs
2. Basic Structures: Sets, Functions, and Relations
3. Algorithms and Complexity
4. Integers and Number Theory
5. Induction and Recursion
6. Counting and Combinatorics
7. Discrete Probability
8. Graph Theory
9. Boolean Algebra and Logic Circuits
10. Formal Languages and Automata Theory
Discrete Probability
Introduction to Discrete Probability
Experiments, Sample Spaces, and Events
Random Experiments
Sample Space
Events as Subsets
Event Operations
Probability of an Event
Definition of Probability
Properties of Probability
Assigning Probabilities
Equally Likely Outcomes
Relative Frequency
Subjective Probability
Finite Probability Spaces
Uniform Probability Models
Non-uniform Models
Probability Theory
Axioms of Probability
Kolmogorov Axioms
Consequences of Axioms
Probability of Complements
Complement Rule
Probability of Unions of Events
Addition Rule
Mutually Exclusive Events
Probability of Intersections of Events
Multiplication Rule
Independent Events
Conditional Probability
Definition and Calculation
Multiplication Rule
Law of Total Probability
Independence of Events
Definition of Independence
Pairwise Independence
Mutual Independence
Testing for Independence
Bayes' Theorem
Statement and Proof
Prior and Posterior Probabilities
Applications of Bayes' Theorem
Medical Testing
Spam Filtering
Random Variables
Definition of Random Variable
Discrete Random Variables
Probability Mass Function
Discrete Probability Distributions
Uniform Distribution
Bernoulli Distribution
Binomial Distribution
Geometric Distribution
Poisson Distribution
Expected Value
Definition and Calculation
Linearity of Expectation
Expected Value of Functions
Variance and Standard Deviation
Definition of Variance
Calculation Methods
Standard Deviation
Properties of Variance
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8. Graph Theory