Available courses

LINEAR ALGEBRA AND CALCULUS (AI Integrated)

From Equations to Algorithms: Linear Algebra and Calculus (AI Integrated)

Mathematics is more than abstract formulas—it is the computational engine behind modern engineering and artificial intelligence. In this course, we bridge classical analytical rigor with AI-assisted tools to solve, visualize, and optimize complex engineering systems. Join us as we master matrix algebra, multivariable calculus, and open-source computational tools to model dynamic processes and drive technological innovation.

By the end of this course, you will be able to:

  • Solve & Transform Linear Systems: Master matrix operations, row reductions, iterative techniques, and eigenvalue problems to analyze linear transformations and complex physical systems using Python, SymPy, and AI workflows.

  • Optimize & Approximate Realities: Apply differential calculus, series expansions, Jacobians, and Lagrange multipliers to approximate engineering functions and solve constrained multivariable optimization problems.

  • Evaluate Spatial & Physical Systems: Utilize double and triple integrals across coordinate transformations (polar, cylindrical, spherical) to compute areas, volumes, mass, centroids, and moments in real-world contexts.

Your Goal: To build the foundational mathematical and AI-assisted computational skills you’ll leverage across all engineering disciplines and advanced technologies.

Linear Algebra and Calculus (Lab)

This course provides a comprehensive foundation in linear algebra and multivariable calculus tailored for modern engineering applications. Aligned with NEP 2020 principles, the course bridges theoretical matrix methods, differential calculus, multivariable optimization, and multiple integration with AI-assisted computational tools (Python, SymPy, NumPy, SciPy, and Google Colab) for visual analysis, simulation, and verification.

Mathematics for Machine Learning and AI

Welcome to Mathematics for Machine Learning and AI!

Have you ever wondered what actually makes Machine Learning models work under the hood? Behind every intelligent algorithm—from image recognition and recommendation engines to advanced AI—lies a foundation of pure mathematics.

This course bridges the gap between theoretical math and real-world AI applications. Over the course of 5 core modules, you will explore how Linear Algebra, Probability & Statistics, Optimization, Vector Calculus, and Graph Theory directly power modern machine learning techniques. By the end of this course, you won’t just be using AI tools as black boxes—you’ll understand the math that makes them tick, allowing you to design, optimize, and evaluate intelligent algorithms with confidence.

23A35101 - Numerical and Statistical Methods

From Data to Design: Numerical & Statistical Methods

Mathematics is more than just equations—it is the tool that makes civil engineering possible. In this course, we move beyond the whiteboard to solve real-world problems using numerical simulations and statistical modeling. Join us as we master the techniques needed to interpret data, ensure structural safety, and drive engineering innovation.

By the end of this course, you will be able to:

  • Solve Complex Systems: Apply numerical methods to solve algebraic, transcendental, and differential equations that describe physical systems.

  • Drive Data-Based Decisions: Use probability theory and hypothesis testing to validate experimental results and ensure safety in civil applications.

  • Model Reality: Transform raw data into predictive mathematical models using interpolation, curve fitting, and regression techniques.

Your Goal: To build the analytical skills you’ll use in the field every single day.