Friday, 11:00 - 12:00, G2K2

1. Week 1 (Statistics, Probability and Python Basics)
   • Python Basics [NB1] [NB2]
   • Pandas Basics [NB]
   • Mean, Median, Mode [NB]
   • Variance and Standard Deviation [NB]
   • Probability density/mass functions [NB]
   • Percentiles and Moments [NB]
   • Crash course on MatPlotLib [NB]
   • Covariance and Correlation [NB]
   • Conditional Probability [NB1] [NB2]
2. Week 2 (Predictive Models)
   • Linear Regression [NB]
   • Polynomial Regression [NB]
   • Multivariate Regression [NB]
3. Week 3 (Machine Learning with Python)
   • Supervised vs. Unsupervised Learning, and Train/Test [NB]
   • Bayesian Methods: Concepts [NB]
   • K-means clustering [NB]
   • Decision Trees [NB] [Data]
   • Support Vector Machines [NB]
4. Recommender Systems
   • User-based and item-based Filtering [NB]