# pseudofish

technology

## CS229 Machine Learning at Stanford (iTunes U)

Andrew Ng’s course at Stanford on Machine Learning provides a great overview of the different types and applications of current machine learning algorithms. The course is available for free either on YouTube or subscribe in iTunes University.

Be warned, this is a course on theory, so is taught through mathematics rather than programming. You will develop a deeper understanding, but it may hurt your brain.

The section notes provide some assistance in reviewing the key concepts that you’ll need.

I’ve watched the course, but I keep forgetting which lesson covered which topic. To remember, I’ve put together a brief summary below:

• 1 — Introduction and overview.
• 2 — Linear regression, Gradient Descent, and [Normal Equations][].
• 3 — Linear regression & probabilistic interpretation, logistic regression (classification algorithm), Newton’s method and a brief introduction to the Perceptron algorithm.
• 4 — Logistic regression & Newton’s method, [Exponential Family][], Generalised Linear Models (GLMs).
• 5 — Generative learning algorithms, [Gaussian Discriminate Analysis][] (GDA), Generative vs Discriminate algorithms, [Naive Bayes][], and Laplace Smoothing.
• 6 — Naive Bayes & Event Models, Neural Networks, [Support Vector Machines][] (SVMs).
• 7 — Optimal Margin Classifier, Primal/Dual optimisation problem (KKT), SVM dual, Kernels.
• 8 — SVMs (Kernels, Soft Margin, SMO algorithm)
• 9 — Learning theory: Bias/Variance, Empirical Risk Minimisation (ERM), Union Bound/Hoeffding inequality, [Uniform Convergence][].
• 10 — VC dimension, Model selection (cross validation, feature selection), Bayesian statistics & regularisation.
• 11 — Bayesian statistics & regularisation, Online Learning, Advice for applying ML algorithms.
• 12 — Clustering (k-means), Mixture of Gaussians, [Jensen’s Inequality][], Expectation Maximisation (EM)
• 13 — Mixture of Gaussians, Mixture of Naive Bayes, [Factor Analysis][], and more on Gaussian distributions.
• 14 — Factor Analysis (EM steps), Principal Component Analysis (PCA).
• 15 — PCA: Latent Semantic Indexing (LSI), [Singular Value Decomposition][] (SVD) algorithm, and [Independent Component Analysis][] (ICA).
• 16 — Markov Decision Process (MDPs), Value Function, [Value Iteration][], and Policy Iteration.
• 17 — Reinforcement learning, using Markov Decision Processes (MDPs), scaled up to continuous variables. Motivating example is controlling an inverted pendulum. Models of system to control can either be derived (eg. physics model) or learned (eg. via a helicopter pilot)
• 18 — State Action Rewards, Finite Horizon MDPs, Linear Dynamic Systems (Models, Linear Quadratic Regulation (LQR), [Riccati equation][]).
• 19 — Debugging RL algorithms, LQR (Differential Dynamic Programming (DDP)), Kalman Filters, Linear Quadratic Gaussians (LQG).
• 20 — POMDPs (Partially Observable MDPs), Policy Search, Reinforce, Pegasus, Conclusion.

iTunes University is a great way to keep up to date with a range of topics. Worth checking out to see what else you can find.