Semester.ly

Johns Hopkins University | EN.540.405

Modern Data Analysis and Machine Learning for Chembes

3.0

credits

Average Course Rating

(4.22)

This class will provide an introduction for chemical and biomolecular engineering students to modern methods of measuring and testing hypotheses using experimental or computational data. The course will cover methods of regression and data analysis such as linear and nonlinear regression, Bayesian analysis and principal or independent component analysis. The course will introduce concepts of machine learning including linear and nonlinear separation, neural networks, Gaussian processes and will provide exposure to deep learning concepts. The course will focus generally on image data and will consider topics of image processing, feature extraction and will cover for general data dimensionality reduction. Familiarity with computer programming (ideally Python), statistics and linear algebra are prerequisites.

Spring 2013

(4.45)

Spring 2014

(4.0)

Spring 2013

Professor: Rebecca Schulman

(4.45)

The best aspects of this course included the hands-on work, the opportunity to design one’s own experimental methods and chemical mechanisms from scratch, and the chance to work in groups. One student felt the required workload was excessive. Another student felt that clearer guidelines should have been given to the students. Suggestions included al owing students to know what each sub-team was working on, and incorporating stricter deadlines for completing the various components. Prospective students should know that they are tested and expected to present on a weekly basis. 59

Spring 2014

Professor: Rebecca Schulman

(4.0)

The best aspects of this course were the hands on design and project base of the course, the freedom to design new things and figure out solutions, and the chance to be creative. Many students thought the relaxed atmosphere of the class made it feel more like a weekend project than a graded class. However, with the freedom of the course came some disorganization in the schedule and uneven distribution of work. Suggestions for improving the course include better structure in the schedule of work due, having two cars to work on, and opportunities to work with CAD. Prospective students should know the class is 66 very laid back and is more like a club, the work can be uneven with some having more responsibility than others, but overall the class is a great way to get hands on experience and is highly recommended.

Lecture Sections

(01)

No location info
R. Schulman
16:30 - 17:45