CS 446
Fall 2025 Part of Term 1
Aug 25-Dec 10
Credit: 3 OR 4 hours.
Principles and applications of machine learning. Main paradigms and techniques, including discriminative and generative methods, reinforcement learning: linear regression, logistic regression, support vector machines, deep nets, structured methods, dimensionality reduction, k-means, Gaussian mixtures, expectation maximization, Markov decision processes, and Q-learning. Application areas such as natural language and text understanding, speech recognition, computer vision, data mining, and adaptive computer systems, among others.
Same as ECE 449. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225; One of MATH 225, MATH 257, MATH 415, MATH 416, ASRM 406 or BIOE 210; one of CS 361, STAT 361, ECE 313, MATH 362, MATH 461, MATH 463, STAT 400 or BIOE 310.
| CRN | Type | Section | Time | Day | Location | Instructor | Section Details | |
|---|---|---|---|---|---|---|---|---|
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46793
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Online
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B3
|
ARRANGED
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n.a.
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n.a.
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Gui, L
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80991
|
Online
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BG
|
ARRANGED
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n.a.
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n.a.
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Gui, L
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46792
|
Online
|
BU
|
ARRANGED
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n.a.
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n.a.
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Gui, L
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81003
|
Online
|
DS3
|
ARRANGED
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n.a.
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n.a.
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Gui, L
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60403
|
Online
|
DS4
|
ARRANGED
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n.a.
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n.a.
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Gui, L
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77676
|
Discussion/
Recitation
Online
|
MC3
MC3
|
ARRANGED
ARRANGED
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n.a.
n.a.
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Location Pending
n.a.
|
Gui, L
Gui, L
|
|
|
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77674
|
Discussion/
Recitation
Online
|
MC4
MC4
|
ARRANGED
ARRANGED
|
n.a.
n.a.
|
Location Pending
n.a.
|
Gui, L
Gui, L
|
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