Course

Syllabus

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 or biomedical use-cases. 

Learning Objectives 

LO1: Understand and apply core concepts of linear algebra such as vectors, matrices, eigenvalues/eigenvectors, and linear transformations for solving computational problems.

LO2: Develop a foundational understanding of probability theory, including random variables and probability distributions, to model uncertainty in engineering and data-driven systems.

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 biomedical or AI-related scenarios mathematically, interpreting data to derive insights and propose scientifically sound solutions.

1. Data Mining Concepts and Techniques by Jiawei Han and Micheline Kamber
2. An Introduction to Probability and Statistics by Rohatgi and Saleh.
3. Business Analytics: Data Analysis and Decision Making by Christian Albright and Wayne Winston