EECS 445 - Introduction to Machine Learning
Section: 002
Term: WN 2018
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)
 
28375
Open
10
 
-
MW 4:30PM - 6:00PM
Note: STUDENTS MUST ELECT DISCUSSION AND LECTURE SECTIONS.Students who do not meet major restrictions can request permission for this course on or after Monday, December 11, 2017 through the CSE Undergraduate Advising Office.
002 (LEC)
 
36103
Open
11
 
-
MW 9:00AM - 10:30AM
011 (DIS)
P
24198
Open
1
 
-
F 11:30AM - 12:30PM
012 (DIS)
P
26321
Open
4
 
-
F 10:30AM - 11:30AM
013 (DIS)
P
26322
Open
6
 
-
F 12:30PM - 1:30PM
014 (DIS)
P
36108
Open
2
 
-
F 1:30PM - 2:30PM
015 (DIS)
P
36109
Open
3
 
-
Th 5:00PM - 6:00PM
016 (DIS)
P
36111
Open
5
 
-
Th 3:30PM - 4:30PM
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