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Maths for Computer Science - Skill Up
Course Description
Maths for Computer Science builds a strong mathematical foundation for students and professionals. Covering Number Theory, Probability, Linear Algebra, and more, the course links theory to practical applications in cryptography, machine learning, and data analysis. Through clear lessons and real-world examples, learners gain the skills needed to solve complex computing problems.
Course Overview
This course is designed to provide computer science students with a robust mathematical foundation, encompassing critical areas such as Number Theory, Probability, Linear Algebra, Statistics, and more. It aims to equip learners with essential tools and concepts to address mathematical challenges in computer science effectively.
The course has each week dedicated to a specific mathematical domain. Daily topics are paired with daily practice quizzes.
Course Highlights
- Understand number systems, primes, and modular arithmetic for cryptography and algorithm design.
- Master random variables, distributions, and theorems like Bayes for machine learning applications.
- Learn vectors, matrices, and transformations for graphics, AI, and optimization problems.
- Gain skills in data analysis, hypothesis testing, and decision-making with real-world relevance.
- Access curated GeeksforGeeks articles and problem sets for hands-on practice.
- Build confidence for interviews and exams with a focus on mathematical problem-solving.
Course Content
- Introduction to number systems.
- Types of number systems and conversions (decimal, binary, octal, hexadecimal).
- Basic concepts including HCF, LCM, and Euclids division algorithm.
- Primes, their significance in cryptography, and prime factorization.
- Miscellaneous concepts like Catalan numbers, Fibonacci sequence, and the pigeonhole principle.
- Modular arithmetic and its applications.
- Foundational theorems such as the Chinese Remainder Theorem, Fermats Little Theorem, and Eulers Theorem
- Introduction to probability, sample spaces, and event types.
- Conditional probability and Bayes theorem.
- Random variables, probability distributions, expected value, and variance.
- Discrete probability distributions (uniform, binomial, Poisson).
- Continuous probability distributions.
- Law of large numbers.
- Moment generating functions.
- Introduction to statistics and its types.
- Descriptive statistics for data summarization.
- Inferential statistics for predictions and decision-making.
- Central limit theorem and its importance.Hypothesis testing fundamentals.
- Parametric methods (Z-test, T-test, ANOVA, Pearson correlation).
- Non-parametric methods (Chi-square test, Mann-Whitney U test, Wilcoxon signed-rank test).
- Scalars, vectors, and matrices basics.
- Vector operations (addition, dot product, cross product).
- Types of matrices, matrix operations, and transformation matrices.
- Linear transformations, eigenvalues, and eigenvectors.
- Gaussian elimination and solving systems of linear equations.
- Singular Value Decomposition (SVD) and LU decomposition.
- Vector norms and vector spaces
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