Overview

 

Applied Machine Learning

 

 

Instructor: Andreas Mueller
Lectures: Mondays and Wednesdays 4:10pm-5:25pm
Room: 408 Zankel Dates: First class 1/18, last class 5/12
Class directory: W4995-20171-005

Office Hours

  • Andreas Müller (lecturer) Wednesday 2pm-4pm, 410 Mudd
  • Akshay Khatri (CA) Tuesdays 12am-2pm, CS TA Room
  • Aarshay Jain (CA) Fridays 5pm-7pm, CS TA Room
  • Rohan Pitre (CA) Thursdays 1pm-3pm, CS TA Room
  • Sheallika Singh (CA) Thursdays 3:20pm-5:20pm, CS TA Room
  • Shiemi Lim (CA) Fridays 1pm-3pm, CA TA Room

Description

This class offers a hands-on approach to machine learning and data science. The class discusses the application of machine learning methods like SVMs, Random Forests, Gradient Boosting and neural networks on real world dataset, including data preparation, model selection and evaluation. This class complements COMS W4721 in that it relies entirely on available open source implementations in scikit-learn and tensor flow for all implementations. Apart from applying models, we will also discuss software development tools and practices relevant to productionizing machine learning models.

Prerequisites

Familiarity with Python programming and basic use of NumPy, pandas and matplotlib.

Grading / course grade

5 homework assignments (60%), midterm exam (20%), final in-class exam (20%). All homework assignments are programming assignments and need to be submitted via Github (as will be explained in the class). The midterm will test material from the first half of the class, while the second exam will test material from the second half.

Exams

The exams will be written, no computer or course material allowed. Everything that is on the slides or on the notes to the slides is up for testing. There might be some minor coding, but mostly conceptual questions and multiple choice.

The syntax of git and the python libraries that were covered in class (as far as they were covered) will be content of the exam.