
Welcome to your Moodle site
Now, you are in control!
Here are some links of interest:
Skip available coursesAvailable courses
This course covers:
 Independence, Probability Rules and Simpson’s Paradox,
 Probability Densities, Expectation, Variance and Moment,
 Examples of Discrete Probability Mass Functions,
 Examples of Continuous Probability Density Functions,
 Functions of Continuous Random Variables,
 Conjugate Probability Distributions,
 Graphical Representations.
This course covers:
 Inverse Transform Sampling,
 Rejection Sampling,
 Importance Sampling,
 Markov Chains,
 Markov Chain Monte Carlo.
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:
 Quadratic Discriminant Analysis,
 Kernel Trick,
 k Nearest Neighbours 123
 Decision Trees,
 Neural Networks,
 Boosting and Cascades.
This course covers:
 K Means Clustering,
 Mixture Models,
 Gaussian Mixture Models,
 ExpectationMaximization,
 Bayesian Mixture Models,
 The Chinese Restaurant Process,
 Dirichlet Process.
This course covers:
 Principal Component Analysis,
 Probabilistic View,
 ExpectationMaximization,
 Factor Analysis,
 Kernel Principal Component Analysis.
This course covers:
 Problem description,
 Linear Regression,
 Polynomial Regression,
 Ordinary Least Squares,
 Over and Underfitting,
 Bias and Variance,
 Crossvalidation,
 Multicollinearity and Principal Component Regression,
 Partial Least Squares,
 Regularization,
 Bayesian Regression,
 Expectation–Maximization,
 Bayesian Learning,
 Gaussian Process.
This course covers:
 Neural Networks,
 Error Backpropagation,
 Autoencoders,
 Autoencoder Example,
 Relationship to Other Techniques,
 Indian Buffet 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:
 Simultaneous Linear Equations,
 Gaussian Elimination and Pivoting,
 LU Factorization,
 Cholesky Factorization,
 QR Factorization,
 The Gram–Schmidt Algorithm,
 Givens Rotations,
 Householder Reflections,
 Linear Least Squares,
 Singular Value Decomposition,
 Iterative Schemes and Splitting,
 Jacobi and Gauss–Seidel Iterations,
 Relaxation,
 Steepest Descent Method,
 Conjugate Gradients,
 Krylov Subspaces and PreConditioning,
 Eigenvalues and Eigenvectors,
 The Power Method,
 Inverse Iteration,
 Deflation.
This course covers:
 Lagrange Form of Polynomial Interpolation,
 Newton Form of Polynomial Interpolation,
 Polynomial Best Approximations,
 Orthogonal polynomials,
 LeastSquares Polynomial Fitting,
 The Peano Kernel Theorem,
 Splines,
 BSpline.
This course covers:
 Bisection, Regula Falsi, and Secant Method,
 Newton’s Method,
 Broyden’s Method,
 Householder Methods,
 Muller’s Method,
 Inverse Quadratic Interpolation,
 Fixed Point Iteration Theory,
 Mixed Methods.
This course covers:
 MidPoint and Trapezium Rule,
 The Peano Kernel Theorem,
 Simpson’s Rule,
 Newton–Cotes Rules,
 Gaussian Quadrature,
 Composite Rules,
 MultiDimensional Integration,
 Monte Carlo Methods.
This course covers:
 Finite Differences,
 Differentiation of Incomplete or Inexact Data.
This course covers:
 OneStep Methods,
 Multistep Methods, Order, and Consistency,
 Order Conditions,
 Stiffness and AStability,
 Adams Methods,
 Backward Differentiation Formulae,
 The Milne and Zadunaisky Device,
 Rational Methods,
 Runge–Kutta Methods.
This course covers:
 Classification of PDEs,
 Parabolic PDEs,
 Finite Differences,
 Stability and Its Eigenvalue Analysis,
 Cauchy Problems and the Fourier Analysis of Stability,
 Elliptic PDEs,
 Computational Stencils,
 Sparse Algebraic Systems Arising from Computational Stencils,
 Hockney Algorithm,
 Multigrid Methods,
 Parabolic PDEs in Two Dimensions,
 Hyperbolic PDEs,
 Advection Equation,
 The Wave Equation,
 Spectral Methods,
 Spectral Solution to the Poisson Equation,
 Finite Element Method.