Spring 2017 - Elements of Artificial Intelligence
The documents within this repository are not intended to serve as fully replicable programs. These were written in python, some python2 and some python3. The class included some assignments where I was paired with another student; that student's name is provided within header comments of each python file.
The class itself was designed to teach students how to implements popular artificial intelligence concepts from scratch. These concepts do not use popular libraries like sklearn to produce results but were implemented with core mathematics involved.
Programs will not run as the basis data is not included. The purpose of providing this courseowrk is only to demonstrate my exposure to these algorithms.
This class was taken after having only 2 months of experience in python and
I've chosen to include this very ugly code as a testement to how I have improved
in a short period of time and since taking this course.
Here are a couple of the concepts learned from this course:
- BFS/DFS Searches
- A*
- Bayes Nets
- Hidden Markov Models
- Viterbi
- Markov Chain Monte Carlo
- Decision Trees
- Linear Models
- Neural Nets
- Support Vector Machines
- Expectation Maximization
- Minimax
- Convolutional NN