- Week 1:
- Mon, Jan 12: Intro - Overview and KNN
- Wed, Jan 14: Review of Calc I - Functions, derivatives, and optimization
- Fri, Jan 16: Overview of Calc III - Functions of two variables and their visualization. Partial derivatives and optimization.
- Week 2:
- Wed, Jan 21: Systems and Matrices - General systems and equation operations, matrix representation and row operation, Reduced Row Echelon Form and nonsingular matrices
- Fri, Jan 23: Matrix algebra - Matrix multiplication, inverses, determinants, and transposes
- Week 3:
- Mon, Jan 26: Linear Algebra I - Abstract vector spaces, linear transformations and their matrix representation
- Wed, Jan 28: Review
- Fri, Jan 30: Exam I
- Week 4:
- Mon, Feb 2: Linear Algebra II - Subspaces, column space, null space, and range
- Wed, Feb 4: Linear Algebra III - Geometry of linear transformations and determinants
- Fri, Feb 6: Linear Algebra IV - Norms and inner products
- Week 5:
- Mon, Feb 9: Data Intro - Data tables and data frames, types of data, images of data.
- Wed, Feb 11: Measures of data - Mean, variance, covariance, and correlation.
- Fri, Feb 13: Linear Regression - Normal equations, simple examples, and Massey ratings
- Week 6:
- Mon, Feb 16: Practical issues - Encoding variables and regularization for linear regression.
- Wed, Feb 18: Review
- Fri, Feb 20: Exam II
- Week 7:
- Mon, Feb 23: Integration - Review of integration and the area problem; u-substitution
- Wed, Feb 25: Lab 1 - Linear regression on the computer
- Fri, Feb 27: Numerical integration - The normal and logistic functions
- Week 8:
- Mon, Mar 2: Discrete probability - Discrete distributions, random variables, and binomial coefficients.
- Wed, Mar 4: Continuous probability - Continuous distributions, random variables, and the normal distribution.
- Fri, Mar 6: More probability - Modeling data, the central limit theorem, and maximum likelihood.
- Week 9:
- Mon, Mar 16: Logistic regression - Probability and classification
- Wed, Mar 18: Lab 2 - Logistic regression for Kaggle’s NCAA competition.
- Fri, Mar 20: Eigenspaces - Definition, computation, and interpretation of eigenvalues and eigenvectors.
- Week 10:
- Mon, Mar 23: Diagonalization - Application of eigenspaces to understand geometric action
- Wed, Mar 25: Review
- Fri, Mar 27: Exam III
- Week 11:
- Mon, Mar 30: Eigenrating - Dominant eigenvectors, Google Pagerank, and another sports rating algorithm.
- Wed, Apr 1: PCA - Principal Component Analysis
- Fri, Apr 3: More optimization - Gradient descent and Lagrange multipliers
- Week 12:
- Mon, Apr 6: SVM 1 - Support vector machines (With potential classification review)
- Wed, Apr 8: SVM 2 - Support vector machines II
- Fri, Apr 10: Lab 3 - SVM Lab
- Week 13:
- Mon, Apr 13: Networks - Definitions and algorithms
- Wed, Apr 15: Review
- Fri, Apr 17: Exam IV
- Week 14:
- Mon, Apr 20: Expression graphs - Representing algebraic expressions as networks.
- Wed, Apr 22: Neural networks - The basics of neural networks.
- Fri, Apr 24: Convolution - Convolutional neural networks
- Week 15:
- Mon, Apr 27: Review
Class Outline
Here’s the tentative outline for MML: