Probabilistic Models

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An Introduction to Probabilistic Machine Learning (openHPI)

Probabilistic machine learning has gained a lot of practical relevance over the past 15 years as it is highly data-efficient, allows practitioners to easily incorporate domain expertise and, due to the recent advances in efficient approximate inference, is highly scalable. Moreover, it has close relations to causal inference which [...]

Probabilistic Graphical Models 2: Inference (Coursera)

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine [...]

Natural Language Processing with Probabilistic Models (Coursera)

In Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming; b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is important for computational linguistics, c) Write a better auto-complete algorithm using [...]

Computational Probability and Inference (edX)

Learn fundamentals of probabilistic analysis and inference. Build computer programs that reason with uncertainty and make predictions. Tackle machine learning problems, from recommending movies to spam filtering to robot navigation.