###Machine Learning Course at CSU East Bay
These are the two projects that I did in the machine learning course I took in graduate school. Included are the project 1 instuctions and the csv files for both project 1 and 2 (1987.csv for project 1 and train.csv & test.csv for project 2). Project 2 was part of the "Titanic: Machine Learning from Disaster" Kaggle competition. Here is a link to the Kaggle page describing the problem.
Both of the projects are implemented in R, as the entire course was taught using R.
This course covered the following topics:
- Feature Scaling
- R Programming
- K-NN
- Naive Bayes and Text Classification
- Decision Trees and Rules
- Sparce Matrices
- Linear Regression
- Regression Trees and Model Trees
- Model Evaluation
- Artificial Neural Networks
- Support Vector Machines (SVMs)
- Regularization
- Parallel Processing and Distributed Systems
- Parameter Tuning
- Boosting, Bagging, and Ensembles
- Affinity Analysis and Association Rules
- Clustering and K-Means
- Cross-validation
- MySQL and Relational Databases