Bayesian Reasoning and Machine Learning

1: Probabilistic Reasoning 2: Basic Graph Concepts 3: Belief Networks 4: Graphical Models 5: Efficient Inference in Trees 6: The Junction Tree Algorithm 7: Making Decisions 8: Statistics for Machine Learning 9: Learning as Inference 10: Naive Bayes 11: Learning with Hidden Variables 12: Bayesian Model Selection 13: Machine Learning Concepts 14: Nearest Neighbour Classification 15: Unsupervised Linear Dimension Reduction 16: Supervised Linear Dimension Reduction 17: Linear Models 18: Bayesian Linear Models 19: Gaussian Processes 20: Mixture Models 21: Latent Linear Models 22: Latent Ability Models 23: Discrete-State Markov Models 24: Continuous-State Markov Models 25: Switching Linear Dynamical Systems 26: Distributed Computation 27: Sampling 28: Deterministic Approximate Inference Mixture Models 21: Latent Linear Models 22: Latent Ability Models 23: Discrete-State Markov Models 24: Continuous-State Markov Models 25: Switching Linear Dynamical Systems 26: Distributed Computation 27: Sampling 28: Deterministic Approximate Inference