
- Teacher: Nasif Khalid Swadheen
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Welcome to the Exciting World of American Literature! 📖✨In this course, we’ll explore the voices that shaped American literary tradition, from Emerson to Miller via Frost. Get ready for engaging discussions, critical insights, and a deeper appreciation of American literature ! In this Course we will be discussing · The American Scholar by Ralph Waldo Emerson · The Road not Taken by Robert Frost · I Heard a Fly Buzz when I Died by Emily Dickinson · Song of Myself by Walt Whitman · The Scarlet Letter by Nathaniel Hawthorne · Death of a Salesman by Arthur Miller
This course examines the works of some major British Romantic poets.
Discrete mathematics is a branch of mathematics that deals with finite and discrete objects, such as integers, sets, graphs, logic, and algorithms. It is often used as a foundation for computer science, software engineering, cryptography, data science, and other fields that involve computation and information. In this course the following topics will be covered: Logic and proofs: how to use symbols, truth tables, and rules of inference to reason about propositions, predicates, and quantifiers. Sets and functions: how to define, manipulate, and compare sets, relations, functions, and cardinality. Number theory and cryptography: how to use modular arithmetic, divisibility, prime numbers, and congruences to encrypt and decrypt messages, and to test the security of cryptographic schemes. Combinatorics and probability: how to count and enumerate objects, permutations, combinations, binomial coefficients, and apply the principles of inclusion-exclusion, pigeonhole, and induction. Recursion and recurrence relations: how to define and solve problems that involve recursive definitions, such as Fibonacci numbers, factorial, and Tower of Hanoi. Graph theory and algorithms: how to represent, traverse, and analyze graphs, trees, networks, and their properties, such as connectivity, coloring, Eulerian and Hamiltonian paths, and planarity. Computability and complexity: how to classify problems and algorithms according to their decidability, solvability, and efficiency, using concepts such as Turing machines, halting problems, NP-completeness, and big-O notation.