pabloschall/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 ](https://github.com/mGalarnyk/DSE220_Machine_Learning/blob/master/IPynb/Assignment_2_MAS.ipynb)

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

My Homework (From Newest to Oldest)

[Assignment 3: Stacking emsemble dataset,Random Forest, etc. ](https://github.com/mGalarnyk/DSE220_Machine_Learning/blob/master/Assignments/Assignment_3_.pdf) (1, 2, 3)

[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)