I am a Ph.D Courses Person to Machine learning with probabilistic graphical models

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an introduction to probabilistic graphical models, which play an important role in
modern machine learning and artificial intelligence. Various types of graphical models are introduced, and their use in typical application
domains illustrated (e.g., Hidden Markov Models for biological sequence analysis,
Markov Random Fields for Image Processing). The course emphasizes both modeling and
learning aspects of graphical models. With regard to modeling, we discuss
underlying probabilistic (independence) assumptions that need to be made when choosing  a
particular type of model for a given application domain. Also limitations imposed by
the computational complexity of inference tasks for a given model are considered.
With regard to learning, the most important paradigms for learning graphical models
from data are explained, and exemplified by typical learning tasks.