EECS 445 - Introduction to Machine Learning
Section: 001
Term: FA 2017
Subject: Electrical Engineering and Computer Science (EECS)
Department: CoE Electrical Engineering and Computer Science
Credits:
4
Requirements & Distribution:
BS
Enforced Prerequisites:
EECS 281, completed with a minimum grade of C or better.
Advisory Prerequisites:
MATH 214 or equivalent; STATS 250 or equivalent.
BS:
This course counts toward the 60 credits of math/science required for a Bachelor of Science degree.
Repeatability:
May not be repeated for credit.
Primary Instructor:

Theory and implementation of state of the art machine learning algorithms for large-scale real-world applications. Topics include supervised learning (regression, classification, kernal methods, neural networks, and regularization) and unsupervised learning, (clustering, density estimation, and dimensionality and reduction).

EECS 445 - Introduction to Machine Learning
Schedule Listing
001 (LEC)
 
27999
Closed
0
 
-
MW 4:30PM - 6:00PM
Note: STUDENTS MUST ELECT DISCUSSION AND LECTURE SECTIONS. Undergraduate students who do not meet major restrictions can request permission for this course on or after April 17, 2017 through the CSE Undergraduate Advising Office.
011 (DIS)
P
23708
Closed
0
 
-
F 11:30AM - 12:30PM
012 (DIS)
P
26157
Closed
0
 
-
Th 4:30PM - 5:30PM
013 (DIS)
P
26158
Closed
0
 
-
F 1:30PM - 2:30PM
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