Probabilistic Graphical Models: Principles and Techniques
Learn important probabilistic modeling languages for representing complex domains and how the graphic models extend to decision making. Use ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces. Apply the basics of the Probabilistic Graphical Model representation and learn how to construct them, using both human knowledge and machine learning techniques to reach conclusions and make good decisions under uncertainty.
Basic probability theory and algorithm design and analysis.
- Bayesian and Markov networks
- Exact and approximate probabilistic inference algorithms
- Speech recognition
- Biological modeling and discovery
- Message encoding
- Medical diagnosis
- Robot motion planning
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