This course covers:

• Features,
• Projections onto Subspaces,
• Fisher’s and Linear Discriminant Analysis,
• Multiple Classes,
• Online Learning and the Perceptron,
• The Support Vector Machine.

This course covers:

• K Means Clustering,
• Mixture Models,
• Gaussian Mixture Models,
• Expectation-Maximization,
• Bayesian Mixture Models,
• The Chinese Restaurant Process,
• Dirichlet Process.

This course covers:

• Problem description,
• Linear Regression,
• Polynomial Regression,
• Ordinary Least Squares,
• Over- and Under-fitting,
  
• Bias and Variance,
• Cross-validation,
• Multicollinearity and Principal Component Regression,
• Partial Least Squares,
• Regularization,
• Bayesian Regression,
• Expectation–Maximization,
• Bayesian Learning,
• Gaussian Process.

This course covers:

• Floating Point Arithmetic,
• Overflow and Underflow,
• Absolute, Relative Error, Machine Epsilon,
• Forward and Backward Error Analysis,
• Loss of Significance,
• Robustness,
• Error Testing and Order of Convergence,
• Computational Complexity,
• Condition.

This course covers:

• Lagrange Form of Polynomial Interpolation,
• Newton Form of Polynomial Interpolation,
• Polynomial Best Approximations,
• Orthogonal polynomials,
• Least-Squares Polynomial Fitting,
• The Peano Kernel Theorem,
• Splines,
• B-Spline.

This course covers:

• Mid-Point and Trapezium Rule,
  
• The Peano Kernel Theorem,
  
• Simpson’s Rule,
• Newton–Cotes Rules,
• Gaussian Quadrature,
• Composite Rules,
• Multi-Dimensional Integration,
• Monte Carlo Methods.

This course covers:

• One-Step Methods,
• Multistep Methods, Order, and Consistency,
• Order Conditions,
• Stiffness and A-Stability,
• Adams Methods,
• Backward Differentiation Formulae,
• The Milne and Zadunaisky Device,
• Rational Methods,
• Runge–Kutta Methods.