EECS 445 - Introduction to Machine Learning
Section: 001
Term: FA 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)
 
32033
Closed
0
 
82
MW 6:00PM - 7:30PM
Note: STUDENTS ARE AUTO-ENROLLED IN LECTURE WHEN THEY ELECT A DISCUSSION.Course will open to EE UG and ECE Grad who meet course prerequisites on Tuesday, April 10, 2018.
011 (DIS)
P
23034
Closed
0
 
15
F 11:30AM - 12:30PM
012 (DIS)
P
25071
Closed
0
 
15
Th 4:30PM - 5:30PM
013 (DIS)
P
25072
Closed
0
 
13
F 1:30PM - 2:30PM
014 (DIS)
P
32428
Closed
0
 
13
F 12:30PM - 1:30PM
015 (DIS)
P
32429
Closed
0
 
15
F 2:30PM - 3:30PM
016 (DIS)
P
32430
Closed
0
 
11
Th 12:30PM - 1:30PM
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