Homework 1: Time series forecasting, discussed MA and AR models
Homework 2: Discussed ACF and PACF plots
Homework 3: Understanding the ROC curve and logisitc regression
Homework 4: Predictor matrices and feature selection
Homework 5: Use the Yahoo music data set to predict the three most liked and three disliked tracks for every user using rule-based learning
Homework 6: Create your own matrix factorization function in matlab/python
Homework 7: Perform principal component analysis on the four-factor time series from Homework 1
Homework 8: Use PySpark for matrix factorization
Homework 9: Use four different classifiers from PySpark to make predictions on Yahoo music dataset
Homework 10: Develop an ensembling method and use it to make predictions for Yahoo music dataset
Final: Using all of the techniques learned over the semester, make predictions on the Yahoo music dataset and submit to Kaggle. Observe which methods produce the best prediction results.