Fall:
   1. Exploratory Data Analysis
   2. Introduction to Machine Learning
   3. Distributions, Confidence Intervals and Hypothesis testing
   4. Simple and Multiple Linear Regression
   5. Non Linear Regressions and Regularization
   6. Linear Classification
   7. Non Linear Classification
   8. Unsupervised Learning

Winter:
    Deep Learning (more details to come!)

Syllabus