jimdc/uncertain-decision-making

Deicsion Making under Uncertainty and Learning

Decision Making under Uncertainty and Learning

Code and results for the following problems:

1. Implementing a custom version of Value Iteration and computing the Optimal Policy for a Markov Decision Process.

2. Creating a decision tree based on attributes used to determine acceptance of students into graduate school.

3. Building a support vector machine that uses two-class training data, and extracting parameters from it.

4. Replicating the learning process of finding a linear separator by starting with a random perceptron and updating.

5. Determining the minimum error for a single perceptron, and developing a system of connected, multi-layered perceptrons.