CS 446
Spring 2026 Part of Term 1
Jan 20-May 6
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 | |
|---|---|---|---|---|---|---|---|---|
|
68039
|
Discussion/
Recitation
Online
|
CSP
CSP
|
ARRANGED
ARRANGED
|
n.a.
n.a.
|
ARR Illini Center
n.a.
|
Gui, L
Gui, L
|
|
|
|
78279
|
Online
|
DS3
|
ARRANGED
|
n.a.
|
n.a.
|
Gui, L
|
|
|
|
62698
|
Online
|
DS4
|
ARRANGED
|
n.a.
|
n.a.
|
Gui, L
|
|
|
|
69727
|
Discussion/
Recitation
Online
|
MC3
MC3
|
ARRANGED
ARRANGED
|
n.a.
n.a.
|
ARR Illini Center
n.a.
|
Gui, L
Gui, L
|
|
|
|
68040
|
Discussion/
Recitation
Online
|
MC4
MC4
|
ARRANGED
ARRANGED
|
n.a.
n.a.
|
ARR Illini Center
n.a.
|
Gui, L
Gui, L
|
|
|
|
78289
|
Lecture-Discussion
|
P3
|
12:30PM
-1:45PM
|
TR
|
1320 Digital Computer Laboratory
|
Zhang, H
|
|
|
|
39433
|
Lecture-Discussion
|
P4
|
12:30PM
-1:45PM
|
TR
|
1320 Digital Computer Laboratory
|
Zhang, H
|
|
|
|
31421
|
Lecture-Discussion
|
PU
|
12:30PM
-1:45PM
|
TR
|
1320 Digital Computer Laboratory
|
Zhang, H
|
|