## B554: Probabilistic Approaches to Artificial Intelligence

### Class Description

Topics will include some of the Review of probability theory and basic calculus, Graphical model frameworks: Bayes networks, Markov networks, Exact inference: Variable elimination, conditioning, clique trees
Approximate inference: Belief propagation, graph cuts, particle-based inference
Inference as optimization
Optimization techniques: Gradient descent, Newton methods, constrained optimization, stochastic optimization, genetic algorithsm- Learning: maximum likelihood and MAP parameter estimation, structure learning, Expectation-Maximization
Temporal models: Markov chains, hidden Markov models
Applications

### Class Information

**School/Department: **Computer Science

**Semester(s): **Spring

**Capacity: ** 50

### Contact Information

**Instructor: **Martha White - martha@indiana.edu

**Other Contact(s): ** Martha White - martha@indiana.edu

### Other Details

**Prerequisites: ** CSCI-B551 or permission of the instructor.

**Day(s) per week offered: ** Two.

**Applied/Theoretical: ** Balanced.

**Formal Computing Lab?: ** No

**Problem Sets: ** Approximately 6 assignments, a final project, and occasional in-class quizzes. The assignments will include both programming and pen-and-paper problems.

**Comments: ** The course will require some level of mathematical maturity, especially with linear algebra, probability theory, and basic calculus, although we will review the key mathematical concepts.