Semester.ly

Johns Hopkins University | EN.520.651

Random Signal Analysis

4.0

credits

Average Course Rating

(4.55)

The content for EN.520.651 has been revised with greater emphasis on graphical models, parameter estimation and posterior inference. Topics include probability theory, random variables/vectors, hypothesis testing, parameter estimation, directed and undirected graphical models, the EM algorithm, deterministic and stochastic approximations for EM, Markov chains and random sequences. Additional material may be covered as appropriate. The class is theoretical in nature; new concepts are presented via formula derivations and example problems. Homework assignments may require familiarity with Matlab (or an equivalent computational software). Audits not permitted.

Fall 2012

(4.48)

Fall 2013

(4.62)

Fall 2014

(4.55)

Fall 2012

Professor: Sanjeev Khudanpur, Jerry Prince

(4.48)

Students said this course provides a thorough overview of the material, including more advanced concepts. They liked the professor’s teaching style and said the coursework was helpful. They said the homework was time-consuming and sometimes overwhelming. In addition, sometimes the homework tested material that had not been covered in the lectures. Students suggested including more practical examples, and grading al the homework problems instead of just one. Students should have a strong math and probability background for this course. It is difficult and time-consuming, but is a very useful course with widely applicable material.

Fall 2013

Professor: Sanjeev Khundanpur

(4.62)

Students thought that the best aspects of this course were the interesting topics and the professor’s thorough lecturing style. Students found the materials chal enging but refreshingly so, although students did complain that the homework load was intense and the difficult materials sometimes led to confusion. Students suggested that the course be broken up into multiple shorter sessions to give them time to review the materials. They also wanted solutions to homework and the exams posted so they could use these materials when studying. Prospective students should have a solid understanding of probability, statistics, and signal processing and be prepared to take on a heavy course load.

Fall 2014

Professor: Sanjeev Khudanpur

(4.55)

Students praised this course for covering ‘extremely relevant material’ in a systematic and well organized way. Perceived issues with the course varied greatly; multiple students thought that the course had such a tight schedule that it felt as though the instructor rushed through certain topics or that they were awkwardly taught. Suggestions for improvement included a desire by multiple students that the instructor provide them with additional ways to review material; one student requested the 122course have dedicated review sessions while another asked that exams from previous years be provided to current students so they could study from them. Prospective students should know that students found the course had a significant workload and it was necessary to have a strong background in probability and math in order to do well in the course.

Lecture Sections

(01)

No location info
T. Tran
15:00 - 16:20