Linear Algebra
Master rigorous vector space theory and high-performance matrix computations for data science and engineering.
This intermediate course bridges formal algebraic structures with high-throughput computational implementations. Learners analyze foundational theorems including spectral decomposition and rank-nullity while implementing numerical factorizations and dimension reduction algorithms in modern software frameworks. The curriculum prepares candidates for advanced scientific research and production-grade machine learning roles.
Curriculum
Two complete tracks. Study either or both — each has its own exam and certificate.
A mathematically rigorous derivation of vector space geometry, inner products, linear mappings, and canonical forms emphasizing formal proofs.
Formal axiomatic definitions, algebraic structures, coordinate representations, and dimensional theorems.
- Axiomatic Vector Spaces and Linear Subspaces Lab40 min
- Bases, Dimension, and Rank-Nullity Dynamics Lab45 min
- Dual Spaces and Annihilators Lab35 min
Careers this prepares you for
- Machine Learning Engineer
- Quantitative Researcher
- Robotics Perception Engineer
- Scientific Computing Specialist
- Data Scientist