Lecture plan
Schedule.
Week 1.
Mon, 7-Jan.
Computational refresher:
digital representations and
numerical integration
Wed, 9-Jan. Probability theory: univariate
distributions, random number generators, and the central
limit theorem
Week 2.
Mon, 14-Jan.
Statistical measures: moments, characteristic functions, confidence limits
Wed, 16-Jan.
Statistical measures: correlations and correlators, power spectra
Week 3.
Mon, 21-Jan.
Martin Luther King Jr. Day
Wed, 23-Jan.
Markov Chain Monte Carlo
Week 4.
Mon, 28-Jan.
Modeling data: Linear Least Squares, Singular Value Decomposition (SVD)
Wed, 30-Jan.
Modeling data: Maximum Likelihood Estimation (+Bootstrap...)
Week 5.
Mon, 4-Feb.
Hypothesis testing, comparing distributions
Wed, 6-Feb.
SNOW DAY!
Week 6.
Mon, 11-Feb.
Fisher information and optimal experiment design
Wed, 13-Feb.
REVIEW
Week 7.
Mon, 18-Feb.
Presidents' Day
Wed, 20-Feb.
MIDTERM EXAM!
Week 8.
Mon, 25-Feb.
Data science: overview +
Principle Component Analysis (PCA)
Wed, 27-Feb.
Data science: Clustering and classification (KDE, k-nn...)
Week 9.
Mon, 4-Mar.
Data science: case study
Wed, 6-Mar.
Machine learning: overview
Week 10.
Mon, 11-Mar.
Spring Break
Wed, 13-Mar.
Spring Break
Week 11.
Mon, 18-Mar.
Optimization
Wed, 20-Mar.
Wavelets and multiresolution analysis
Week 12.
Mon, 25-Mar.
ODEs: explicit, implicit, symplectic
Wed, 27-Mar.
PDEs: finite difference methods
Week 13.
Mon, 1-Apr.
PDEs: multiple dimensions, SOR, beyond FDM
Wed, 3-Apr.
Simulations
Week 14.
Mon, 8-Apr.
Intro to quantum computing
Wed, 10-Apr.
Project presentations
Week 15.
Mon, 15-Apr.
Review
Wed, 17-Apr.
Presentations
Week 16.
Mon, 22-Apr.
Presentations
Wed, 24-Apr.
Reading Day
