DS4400: Machine Learning and Data Mining I

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 programming 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: Robinson Hall 107

  • Staff:

    • Hongyang Ryan Zhang. OH: Monday 6pm to 7pm (or by email appointment).

    • Haoyu He. OH: TBA.

    • Jivesh Poddar. OH: TBA

Grading: Four problem sets (40%), one midterm exam (30%), one final project report (25%), and class participation which includes project presentation and report discussion (5%).