boost2017 / DSE220_Machine_Learning

Repo for my graduate data science machine learning class at UCSD (UC San Diego). This course provides a broad introduction to the practical side of machine-learning and data analysis. The topics covered in this class include topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting and perceptrons, and topics in unsupervised learning, such as k-means, PCA and Gaussian mixture models.

DSE220_Machine_Learning

Repo for my graduate data science machine learning class at UCSD (UC San Diego). This course provides a broad introduction to the practical side of machine-learning and data analysis. The topics covered in this class include topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting and perceptrons, and topics in unsupervised learning, such as k-means, PCA and Gaussian mixture models.

IPython Notebooks for Assignments (From Newest to Oldest)

Assignment 2: Sabermetrics, Nearest Neighbor Classification, and Housing Prices using Regression Trees
Assignment 1: Pandas, Bash for Basic ETL, and Decision Trees

My Homework (From Newest to Oldest)

[Updated Assignment 4: Clustering ](https://github.com/mGalarnyk/DSE220_Machine_Learning/blob/master/IPynb/4_Clustering.ipynb)

[Updated Assignment 3: Titanic ](https://github.com/mGalarnyk/DSE220_Machine_Learning/blob/master/IPynb/3_Titanic.ipynb)

[Updated Assignment 3: Boston ](https://github.com/mGalarnyk/DSE220_Machine_Learning/blob/master/IPynb/3_Boston_Housing-Copy1.ipynb)

[Updated Assignment 2: Sabermetrics, Nearest Neighbor Classification, and Housing Prices using Regression Trees ](https://github.com/mGalarnyk/DSE220_Machine_Learning/blob/master/IPynb/Assignment_2_MAS.ipynb) (1, 2, 3)

[Assignment 1: Pandas, Bash for Basic ETL, and Decision Trees](https://github.com/mGalarnyk/DSE220_Machine_Learning/blob/master/IPynb/1_AssignGalarnykMichael-Copy1.ipynb) (1, 2,4,5)