###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