Johns Hopkins Machine Learning 600.475 Spring 2014
Schedule: Mon/Wed 1:30-2:45pm
Location: Gilman 50
Instructor: Prof. Mark Dredze
Office hours: Wed 3-4pm (Hackerman 226C)

TA Office hours: Purnima Rajan: Monday 4:30-6pm (NEB/Croft 227, CS undergrad lab)
Yiming Wang: Thursday 2:30-4pm (Hackerman 321)

Contact email:


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.

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