Johns Hopkins Machine Learning 601.475 Fall 2017
Schedule: Mon/Wed 1:30-2:45pm
Recitation (optional): Fri 1:30-2:45pm, starting Sept 15
Location: Shaffer 303
Instructor: Prof. Mark Dredze
Office hours: Monday 3pm (Hackerman 226D)

TA Office hours:
Rob DiPietro Tuesday 1pm (Malone 222)
Andrew Dykman Wednesday 5pm (Malone 239)

Contact email:

Description

This course takes an application driven approach to current topics in machine learning. The course covers supervised learning, unsupervised learning, semi-supervised learning, and several other learning settings. We will cover popular algorithms and will focus on how statistical learning algorithms are applied to real world applications. Students will implement several learning algorithms throughout the semester. The goal of this course is to provide students with the basic tools they need to approach various applications, such as:
  • Biology/Bioinformatics
  • Information Retrieval
  • Natural Language Processing
  • Speech Processing
  • Vision
We will focus on fundamental methods applicable to all applications. Application specific techniques, such as feature extraction, will be covered only to the benefit of understanding the basic methods.

Previous Years