DS5220: Supervised Machine Learning and Learning Theory

Course overview

This course provides an introduction to supervised machine learning and the mathematical grounding behind this subject. Topics include fundamental superivsed learning methods and models, including regression, classification, dimensional reduction, regularization, model selection, and deep neural networks. Hands on examples and theoretical exercises will be discussed along with each topic. By the end of the course, we hope that the student has managed to achieve a pratical and principled understanding of supervised machine learning. It is expected that these understanding will facilitate the student's ability to solve supervised learning problems in the future.

Course requirement:

  • Linear algebra; Probability; Calculus.


  • Time: Monday and Thursday, 11:45am - 1:25pm

  • Location: West Village G 102

  • Staff:

    • Hongyang Ryan Zhang. OH: Thursday 1:30pm to 3pm (or by email appointment).

    • Teaching assitants. TBA.

Grading: Five problem sets (40%), one final project report (20%), final exam (35%), and quiz and class participation (5%).