Build the mathematical meaning.
Teaching & learning design
From mathematical concepts to computational confidence.
I design learning experiences that combine conceptual clarity, mathematical reasoning, Python, visualization and real-world application—helping students understand both how a method works and why it matters.
Lecture note / 07Linear Algebra
Av = λv
Concept → geometric meaning → Python experiment → application
teach the idea,
then test it.
Learning design
A repeatable path from idea to application.
Each topic moves through four connected stages, keeping mathematical understanding and practical application in the same learning journey.
See structures and relationships.
Implement with Python.
Solve a realistic problem.
Course expertise
Mathematics connected with computing and AI.
Selected courses taught or developed for Mathematics and Computer Science students.
Current teaching
Linear Algebra for Machine Learning
Vectors, vector spaces, matrices, transformations, eigenvalues, orthogonality, least squares and SVD with Python and ML applications.
10 lecturesPythonCase studies
Find course content on PMAI Lab ↗Applied mathematics
Python for Mathematics
Symbolic and numerical computation, visualization, experimentation and mathematical problem solving using Python.
SymPyNumPyMatplotlib
Explore Python learning on PMAI Lab ↗Computer science foundation
Discrete Structures
Logic, sets, relations, functions, combinatorics, graphs and mathematical structures supporting algorithms and computation.
LogicGraphsAlgorithms
Find course content on PMAI Lab ↗Mathematical foundation
Calculus & Analytical Geometry
Limits, differentiation, integration, coordinate geometry, modelling, change and optimization through visual and applied learning.
ModellingVisualizationOptimization
Find course content on PMAI Lab ↗Course development
Machine Learning Foundations
Mathematically grounded material connecting data, models, loss functions, optimization and evaluation.
MLMathematicsPython
Explore ML content on PMAI Lab ↗Faculty development
Python for Beginners
Accessible, browser-based introduction to Python using Google Colab, practical examples and guided activities.
BeginnersColabWorkshop
Explore Python content on PMAI Lab ↗PMAI Lab learning platformContinue from course overview to full learning materials.
Explore complete PMAI course catalog ↗Browse Mathematics, Computer Science, Artificial Intelligence and Data Science courses, with notes, assignments, quizzes, projects and practical resources as they are published.
Learning resources
Materials designed for continued learning.
Resources are structured so students can revisit concepts after class and reproduce the computational work independently.
Lecture pathway
Linear Algebra for Machine Learning from vectors to SVD.
Colab-ready Python
Executable code connected directly to the mathematics.
Applied case studies
Scenario-based work connecting concepts with decisions and data.
Interactive learning
Visual activities, mini experiments and reflective questions.
For every learner
“Growth begins when we learn with curiosity, unlearn with courage, and relearn with purpose.”Keep questioning. Keep building.
Teaching collaboration