Advanced-Machine-Learning

TDDE15 - Advanced Machine Learning | Linköpings university

Lab 1 contains

• Graphical models
• Hill-climbing heuristics that generates Bayesian networks
• Markov blankets
• Naive Bayes classifier built through a Bayesian network

Lab 2 contains

• Implementation of a Hidden Markov Models
• Simulations from a Hidden Markov Model
• Filtered and smoothed probability distributions for different time step
• Viterbi algorithm
• Hidden probabilities of a future, hidden time step

Lab 3 contains

• Particle filter
• Implementation of a State-Space Models from given transition, emission and initial models
• Elaboration of standard deviation within the emission model
• Elaboration with the weights of the particles