| dc.contributor.author |
Massachusetts Institute of Technology. Media Laboratory. |
|
| dc.coverage.temporal |
Fall 2000 |
|
| dc.date.accessioned |
2008-08-14T04:53:16Z |
|
| dc.date.available |
2008-08-14T04:53:16Z |
|
| dc.date.issued |
2000-12 |
|
| dc.identifier |
MAS.622-Fall2000 |
|
| dc.identifier.other |
MAS.622 |
|
| dc.identifier.other |
1.126J |
|
| dc.identifier.other |
IMSCP-MD5-4daecab298b87ed17e30c68b58fa204f |
|
| dc.identifier.uri |
http://hdl.handle.net/1721.1/41935 |
|
| dc.description.abstract |
Fundamentals of characterizing and recognizing patterns and features of interest in numerical data. Basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non-parametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research. |
en |
| dc.description.provenance |
Submitted by MIT OCW (ocw-dspace@mit.edu) on 2008-08-14T04:53:16Z
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Previous issue date: 2000-12 |
en |
| dc.language.iso |
en-US |
|
| dc.rights |
This site (c) Massachusetts Institute of Technology 2003. Content within individual courses is (c) by the individual authors unless otherwise noted. The Massachusetts Institute of Technology is providing this Work (as defined below) under the terms of this Creative Commons public license ("CCPL" or "license"). The Work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. By exercising any of the rights to the Work provided here, You (as defined below) accept and agree to be bound by the terms of this license. The Licensor, the Massachusetts Institute of Technology, grants You the rights contained here in consideration of Your acceptance of such terms and conditions. |
en |
| dc.subject |
machine and human learning |
en |
| dc.subject |
unsupervised learning and clustering |
en |
| dc.subject |
non-parametric methods |
en |
| dc.subject |
Bayesian estimation |
en |
| dc.subject |
maximum likelihood |
en |
| dc.subject |
statistical classification |
en |
| dc.subject |
decision theory |
en |
| dc.subject |
physiological analysis |
en |
| dc.subject |
computer vision |
en |
| dc.subject |
peech recognition and understanding |
en |
| dc.subject |
recognition |
en |
| dc.subject |
numerical data |
en |
| dc.subject |
MAS.622 |
en |
| dc.subject |
1.126J |
en |
| dc.subject |
1.126 |
en |
| dc.subject.lcsh |
Pattern perception |
en |
| dc.subject.lcsh |
Pattern recognition systems |
en |
| dc.title |
MAS.622 / 1.126J Pattern Recognition & Analysis, Fall 2000 |
en |
| dc.title.alternative |
Pattern Recognition & Analysis |
en |
| dc.audience.educationlevel |
Graduate |
|
| dc.subject.cip |
090101 |
en |
| dc.subject.cip |
Communications Studies/Speech Communication and Rhetoric |
en |
