Course Outcomes
CO1:
Apply fundamental concepts from linear algebra and probability to represent and solve data-centric problems relevant to biomedical engineering and artificial intelligence.
CO2:
Model and analyze real-world scenarios mathematically, interpreting data to derive insights and propose scientifically sound solutions.
Course Contents
Mathematical Foundations – Linear Algebra- Vectors, Matrices, Eigenvalues, Eigenvectors, singular value decomposition, dimensionality reduction, Principal component analysis, linear transformations. Probability and Statistics: Random Variables, Probability Distributions, Distribution functions and properties, Discrete and Continuous, Statistical Inference – Estimation and Hypothesis Testing.
Applied Case Studies & Mathematical Modeling: Data-Driven Problem Solving, Framing real-world biomedical and AI problems mathematically, Building and analyzing mathematical models, Applying linear algebra and probability concepts to interpret data.
Project-Based Learning: Team and individual mini-projects based on industry-inspired use-cases.