## BIOE 210: Spring 2021

Linear Algebra for Biomedical Data Science

#### Course Materials

- Syllabus
*Linear Algebra: Foundations of Machine Learning*
- Practice Problems
- Assignments

#### Schedule

- Tu 1/26: Notation, linearity, and field axioms (0 - 1.5) [slides] [notes]
- Th 1/28: Norms, dot products, and multiplication (1.6 - 2.1) [notes]
- Tu 2/2: Matrix multiplication, rotation, and translation (2.2 - 3.3) [notes]
- Th 2/4: Linear systems and Gaussian Elimination (4)
- Tu 2/9: Finite difference method (5) [notes]
- Th 2/11: Matrix inverse (6) [notes]
- Tu 2/16: Rank and solvability (7) [notes]
- Th 2/18: Linear models I (8) [notes]
- Tu 2/23:
**Exam 1** (chapters 0-7)
- Th 2/25: Linear models II (9) [slides] [Matlab Workbook]
- Tu 3/2: Nonlinear systems and root finding (10) [notes]
- Th 3/4: Optimization and convexity (11) [notes]
- Tu 3/9: Gradient descent and logistic regression (12,13)
- Th 3/11: Cross validation and Regularized regression (14)
- Tu 3/16: Lines and hyperplanes (15)
- Th 3/18: Support Vector Machines (16)
- Tu 3/23: Stochastic Gradient Descent
- Th 3/25:
**Exam 2**
- Tu 3/30: Artificial Neural Networks I: Perceptrons
- Th 4/1: Artificial Neural Networks II: Backpropagation
- Tu 4/6: Artificial Neural Networks III: Training
- Th 4/8: Basis vectors and orthogonality
- Tu 4/13:
*Break: no class*
- Th 4/15: Eigenvalues and eigenvectors
- Tu 4/20: Singular Value Decomposition
- Th 4/22: Low rank approximations
- Tu 4/27: Principal Component Analysis [PCA Infographic]
- Th 4/29: Selected applications
- Tu 5/4:
**Exam 3**