CS-101: Advanced information, computation, communication I

Lectures in this course (309)

Covers logical equivalences in propositional logic, including De Morgan's Laws and Equivalences with Basic Connectives.

Covers the growth of functions and time complexity analysis for search algorithms.

Covers the time complexity of algorithms and includes a quiz.

Explores examples of algorithm complexity, sorting, and polynomial computations.

Analyzes the growth of functions to understand algorithm complexity and efficiency.

Explains Big-O notation for algorithm complexity and polynomial growth rates.

Explores advanced big-O facts for powers, logarithms, factorials, and function combinations.

Explains Big-Omega, Big-Theta, and Little-o notations for functions and polynomials.

Introduces time complexity and worst-case analysis of algorithms, abstracting computational complexity from implementation details.

Covers the complexity analyses of search and sorting algorithms.

Covers matrix notation, arithmetic, multiplication algorithms, and complexity analysis.

Covers constructing and proving logical equivalences, including De Morgan's Laws and tautology proofs.

Covers complexity classes, tractable problems, the class NP, NP-complete problems, and summarizes the concept of tractable problems.

Covers the concepts of mathematical and strong induction through an example.

Covers updates on course materials, a questionnaire, and additional videos.

Covers mathematical induction, recursively defined functions, and iterative algorithms for problem-solving.

Covers topics related to induction and recursion, including mathematical induction proofs and recursive definitions.

Covers examples and a Q&A session on induction and recursion.

Covers the principle of mathematical induction for proving propositions true for all positive integers.

Covers proofs by mathematical induction and the number of subsets of a finite set.

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