MIT Challenge Curriculum

I found that I am not strong in some topics of fundamental maths.100 Everything playlist cover basics but very useful

This playlist is designed to guide you from the very basics of Rust to its most advanced features.

Lecture videos are from Fall 2020 at Northwestern University. This course was formerly known as EECS-340.

Classes teach you all about advanced topics within CS, from operating systems to machine learning, but there’s one critical subject that’s rarely covered, and is instead left to students to figure out on their own.

This calculus course covers differentiation and integration of functions of one variable, and concludes with a brief discussion of infinite series. Calculus is fundamental to many scientific disciplines including physics, engineering, and economics

This course covers differential, integral and vector calculus for functions of more than one variable. These mathematical tools and methods are used extensively in the physical sciences, engineering, economics and computer graphics.

For a double major in Computer Science and General Mathematics, the proof skills acquired from MIT's Mathematics for Computer Science (6.1200J) are not enough on their own.

Covers elementary discrete mathematics for science and engineering, with a focus on mathematical tools and proof techniques useful in computer science.

just for fun

illustrative topics in discrete applied mathematics, including counting, generating functions, probability, linear optimization, algebraic structures, basic number theory, information theory, and coding theory.

This course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines such as physics, economics and social sciences, natural sciences, and engineering.

This course is an introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems.

This course introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies.

This course introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies.

This course emphasizes computability and computational complexity theory. Topics include regular and context-free languages, decidable and undecidable problems, reducibility, recursive function theory, time and space measures on computation, completeness, hierarchy theorems, inherently complex problems, oracles, probabilistic computation, and interactive proof systems.

This course analyzes combinatorial problems and methods for their solution. Topics include: enumeration, generating functions, recurrence relations, construction of bijections, introduction to graph theory, network algorithms, and extremal combinatorics.

This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application.

This is a course on the design and implementation of operating systems and their use as a foundation for systems programming.

This course introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies.