Students who complete this unit will demonstrate that they can:
- Compare and contrast the basic techniques for representing
uncertainty.
- Explain how conditional independence assertions allow for
greater efficiency of probabilistic systems.
- Identify examples of knowledge representations for reasoning
under uncertainty.
- State the complexity of exact inference. Identify methods for
approximate inference.
- Describe and explain the use of Bayes Theorem in artificial
intelligence.
- Make a probabilistic inference in a real-world problem using
Bayes’ theorem to determine the probability of a hypothesis given
evidence.
- Apply the simple statistical learning algorithm such as Naive
Bayesian Classifier to a classification task and measure the
classifier's accuracy.
- Design and implement a probabilistic inference mechanism
using the naive Bayes classifier.