# ELEC 301 - Signals, Systems, and Learning - Fall 2016

This course deals with signals, systems, and transforms, from their theoretical mathematical foundations to practical implementation in circuits and computer algorithms to learning algorithms that convert signals into inferences.

The first half of the course follows the classical "physics" based approach, while the second half follows the more modern "machine learning" approach. In both halves, linear algebra plays a starring role. Fundamentally important, ELEC 301 acts as a bridge between the introductory ELEC 241/2 and more advanced courses such as ELEC 302, 303, 430, 431, 437, 439, etc. in electrical and computer engineering and the broader area of data science.

**Instructor:**

**Location:** Duncan Hall Room 1042

**Time:** Tuesdays/Thursdays 1:00 to 2:15pm

**Prerequisites:** ELEC 241: Fundamentals of Electrical Engineering I, ELEC 242: Fundamentals of Electrical Engineering II, CAAM 335: Matrix Analysis, ELEC 303: Random Signals (co-requisite)

**Additional Information:**

*Course Fellow*

Jeff Lievense

Duncan Hall 2120, lievense@rice.edu

Office hours: TBA

*Weekly Q/A Help Session*

Leaders:

--Morganne Lerch

--Mia Polansky

--Jorge Quintero

Meeting Time: TBD

**Course Syllabus**- Course email: elec301TA@gmail.com Use this email to contact the course staff with questions, comments, etc.
- Piazza Course Management Site.
**It is mandatory that you use this site.**All official announcements will be made through Piazza. - Additional Resources
- Lecture Slides (first half of course)
- Demos and Applets
- MATLAB Tutorial Videos from The MathWorks
- "Signal Processing: A view of the future" Part 1, Part 2 (accessible from Rice campus network)
- Introductory materials on machine learning

http://www.iro.umontreal.ca/~vincentp/Presentations/2015_august_DLSummer

http://www.sciencemag.org/content/349/6245/255.full.pdf - Machine Learning Resources

http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf

http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf

http://alex.smola.org/drafts/thebook.pdf

http://www.szit.bme.hu/%7Egyorfi/pbook.pdf

http://cs229.stanford.edu/materials.html

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6...

http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml

http://ai.stanford.edu/~nilsson/MLBOOK.pdf

http://ciml.info

http://arxiv.org/pdf/0904.3664.pdf

http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mlteach.htm

Wikipedia Machine Learning Portal - https://en.wikipedia.org/wiki/Portal:Machine_learning - Computing resources

Matlab machine learning toolbox - http://www.mathworks.com/solutions/machine-learning/

Python machine learning - http://scikit-learn.org