https://mathresearch.utsa.edu/wiki/index.php?action=history&feed=atom&title=MAT4YYY 2026-06-12T17:02:48Z Revision history for this page on the wiki MediaWiki 1.34.1 https://mathresearch.utsa.edu/wiki/index.php?title=MAT4YYY&diff=5378&oldid=prev 2025-08-19T18:02:29Z

<p>Created page with &quot;=Mathematics of AI - MAT4YYY/5YYY= &#039;&#039;&#039;Catalog description&#039;&#039;&#039; &#039;&#039;Prerequisites&#039;&#039;: Prerequisites: <a href="/wiki/index.php?title=MAT2233" title="MAT2233">MAT2233</a> Linear Algebra, or <a href="/wiki/index.php?title=MAT_253&amp;action=edit&amp;redlink=1" class="new" title="MAT 253 (page does not exist)">MAT 253</a> Applied Linear Algebra; STA3513...&quot;</p> <p><b>New page</b></p><div>=Mathematics of AI - MAT4YYY/5YYY=<br /> <br /> <br /> <br /> '''Catalog description'''<br /> <br /> ''Prerequisites'': Prerequisites:<br /> [[MAT2233]] Linear Algebra, or [[MAT 253]] Applied Linear Algebra;<br /> [[STA3513]] Probability and Statistics<br /> [[MAT2213]] Calculus 3<br /> CS 1063 Introduction to Computer Programming, or CS 1083 Programming I for Computer Scientists.<br /> <br /> ''Content'': <br /> Vectors, matrices and tensors; discrete and continuous probability; random variables and expectation; elements of information theory; basic methods for probabilistic learning (maximum likelihood, Bayesian statistics); supervised and unsupervised learning algorithms; neural networks: feedforward, convolutional, recurrent/recursive; loss functions and gradient-based learning; optimization and regularization methods; algorithm implementations; testing on real-world datasets.<br /> <br /> <br /> == Sample textbook == <br /> <br /> [1] Mathematics of Quantum Computing: An Introduction<br /> by Wolfgang Scherer, Springer, 2019.</div>

Jose.iovino