Semester.ly

Johns Hopkins University | EN.600.775

Selected Topics in Machine Learning

1.0

credits

Average Course Rating

(3.93)

This seminar is recommended for all students interested in data intensive computing research areas (e.g., machine learning, computer vision, natural language processing, speech, computational social science). The meeting format is participatory. Papers that discuss best practices and the state-of-the-art across application areas of machine learning and data intensive computing will be read. Student volunteers lead individual meetings. Faculty and external speakers present from time-to-time. Recommended Course Background: machine learning or permission of the instructor.

Spring 2014

(4.0)

Spring 2015

(3.86)

Spring 2014

Professor: Raman Arora, Mark Dredze, Jason Eisner, Sanjeev Khudanpur, Carey Priebe, Suchi Saria

(4.0)

The highlights of this course are the seminar style, open discussions, and the material presented. The instructor is enthusiastic about the subject but stil approachable. Students learned a great deal on a wide variety of natural resources and the impending energy crisis. Some students thought the grading rubrics were unclear, and that there was more work than expected for a one credit course. Some possible improvements suggested were more clarity and feedback regarding grades, and more of the instructors accumulated knowledge in relation to the readings. Prospective students should know that class participation is a large part of your grade, and if you are interested in issues of sustainability you will enjoy this course.

Spring 2015

Professor: Raman Arora, Mark Dredze

(3.86)

The best aspects of the course included the manageable work load, the weekly topics, and the rock field trip. Students felt the lectures were repetitive and not engaging, and there was a lack of feedback on homework. Suggestions for improvement included spreading out assignments throughout the semester, increasing class interaction and participation, and breaking up the field trip into multiple days as it was fairly long. Prospective students interested in geology or the environment should take this class. This course requires a ten page term paper at the end of the semester.