CS486/686 - Goals


The design of automated systems capable of accomplishing complicated tasks is at the heart of computer science.  Abstractly, automated systems can be viewed as taking inputs and producing outputs towards the realization of some objectives.  In practice, the design of systems that produce the best possible outputs can be quite challenging when the choice of outputs is constrained, the consequences of the outputs are uncertain and/or dependent on other systems, the information provided by the inputs is incomplete and/or noisy, there are multiple (possibly competing) objectives to satisfy, the system must adapt to its environment over time, etc.  This course provides an introduction to Artificial Intelligence, covering some of the core topics that underly automated reasoning.  The modeling techniques that will be covered are quite versatile and can be used to tackle a wide range of problems in many fields including natural language processing (e.g., topic modeling, document clustering),  robotics (e.g.,  mobile robot navigation),  automated diagnosis (e.g., medical diagnosis, fault detection), data mining (e.g., fraud detection, information retrieval), operations research (e.g., resource allocation, maintenance scheduling), assistive technologies, etc.


The topics we will cover include:

  1. Introduction to Artificial Intelligence
  2. Search and Problem Solving
    1. Uninformed Search
    2. Informed Search
    3. Constraint Satisfaction Problems
    4. Propositional Logic
    5. First-order Logic
  3. Reasoning Under Uncertainty
    1. Probability Theory
    2. Bayesian Networks
    3. Utility Theory
    4. Decision Networks
    5. Markov Networks
    6. Markov Logic Networks
  4. Machine Learning
    1. Inductive Learning
    2. Decision Trees
    3. Ensemble Learning
    4. Statistical Learning
  5. Other areas of Artificial Intelligence
    1. Natural Language Processing
    2. Assistive Technologies