Course Syllabus

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Instructor

Joan Bruna, joan.bruna@berkeley.edu, 419 Evans

office hours: Tuesday, 2pm-4pm


GSI

Simon Walter, sjswalter@berkeley.edu

office hours: Wednesday, 5:10-7:10pm, 444 Evans


Class Information

TuTh 12:30-2pm, 160 DWINELLE map

 

Course Objectives

Time series refer to any collection of measurements taken at different points in time. The objective of this course is to present you with the mathematical and statistical tools to analyze such data. We will cover temporal, Fourier and Wavelet analysis, and its applications to modern statistical signal processing and machine learning. 

 

Reference Texts

The main reference text will be "Time Series Analysis and its Applications. With R Examples", by Robert H. Shumway and David S. Stoffer. Springer. 3nd Edition. 2010, available online here.

Another reference we will use in the latter part of the course is "A Wavelet tour of Signal processing: the Sparse way", by S. Mallat, AP.

 

Course Materials

The slides covered during lectures will be posted here, if possible before each lecture. Homework and labs will tentatively use the ipython/jupyter/iRkernel framework.

 

Prerequisites 

Background on probability (stats 134), statistics (101 or 135) and Calculus. Notions of Harmonic Analysis and Signal Processing will be helpful but not required.

 

Grading

The final grade will be a weighted average of the following aspects:

  • Lab/Homework Assignments (25%): posted every one to two weeks, and due on Fridays at the start of the section. The grade will be the average of all homework grades except the worst. 
  • Midterm Exams (30%): scheduled for October 13 and November 12, at the usual lecture time and place. 
  • Final Project (10%): a project in which you will choose and analyze time series data using the concepts learnt in class.
  • Final Exam (35%): scheduled for Friday 12/18/15.

 

Grade Complaints

If you have a complaint against an assigned homework or exam grade and want to talk to me about it, first send me a written request through email explaining your case clearly.

 

Academic Honesty

You are encouraged to work in small groups on homework problems. However, you must write up the solutions on your own, and you must never read or copy the solutions of other students. Similarly, you may use books or online resources to help solve homework problems, but you must credit all such sources in your writeup and you must never copy material verbatim. Any student found to be cheating risks automatically failing the class and being referred to the Office of Student Conduct. In particular, copying solutions, in whole or in part, from other students in the class or any other source without acknowledgment constitutes cheating.

Course Summary:

Date Details Due