Prerequisites:
Since this course has students from varied disciplines it is hard to pin down just 2-3 classes as pre-requisites because you may have obtained the desired knowledge in a number of different ways. The courses I list below as examples should help you assess your preparedness.
If you are an undergraduate (or a graduate student in doubt of your background) please contact me (the instructor). I want to ensure that you have the necessary bases. I will consider and discuss your case with you individually.

To request my permission (or discussion of your preparedness), please send me an email telling me
- Your name
- Your major and year of study
- The courses you took from my list, or equivalent courses. If you list equivalent courses please give a 2-3 line description of what each of them covered.

Students are generally expected to handle mathematics / probability / statistics on 300 level, 400 level is better. Coding in MATLAB, R, or C is expected, or commitment to come up to speed.

Details on desired background:

  • No previous knowledge of Artificial Neural Nets is assumed.
  • Linear algebra (such as in CAAM 335, MATH 355, ELEC 301, ELEC 303, or equivalent), and multivariate calculus (such as in CAAM 336, MATH 321, CAAM 501, or equivalent);
  • Probability and statistics (such as in STAT 310, or ELEC/STAT 331 or equivalent);
  • Simple information theoretical notions (ELEC 241 or equivalent); These will be briefly reviewed in the course.
  • If you have taken (ELEC 531 and (CAAM 583 / ELEC 533 / STAT 583) ) or (STAT 615 and STAT 518) you will automatically qualify after you tell me, do not need to list anything else.

    Additionally, courses such as the following (or similar) are an advantage:
  • ELEC/COMP 440 or ELEC 535; ELEC 478, 483, CAAM 416, 453; ELEC / CAAM / MECH 508; STAT 413 / 613

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