Probabilistic
graphical models (PGM) is one of the most advanced techniques
in machine learning to represent data and models in
the real world with probabilities. PGM present a general
framework for constructing and using probabilistic models
of complex systems that would enable a computer to use
available information for making decisions. This course
is for anyone who has to deal with lots of data and
draw conclusions from it, especially when the data is
noisy or uncertain. Data scientists, machine learning
enthusiasts, engineers, and those who curious about
the latest advances in machine learning will find PGM
interesting |

1. Daphne
Koller, Nir Friedman, Probabilistic Graphical Models-
Principles and Techniques, 1e, MIT Press, 2009
2. Christian Borgelt, Rudolf Kruse and Matthias Steinbrecher,
Graphical Models- Methods for data analysis and Mining,
2e, Wiley, 2009
3. David Bellot, Learning Probabilistic Graphical Models
in R, Packt Publishing, 1e, 2016
4. Luis Enrique Sucar, Probabilistic Graphical Models,
1e, Springer Nature, 2015 |