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            \hbox to 6.28in { {\bf EE 516: Mathematical Foundations of Machine Learning
            \hfill Winter \the\year} }
        \vspace{4mm}
        \hbox to 6.28in { {\Large \hfill Homework #1 \hfill} }
        \vspace{2mm}
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% For problem titles
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\begin{document}

% PUT YOUR NAME BELOW WHERE I HAVE \X
\homework{0}{Januray 13, \the\year}{\X}
% PUT YOUR NAME ABOVE WHERE I HAVE \X

\section{\normalfont{(0 pts)}}

Create a gradescope account on \url{https://gradescope.com}. 
Use course entry code \textbf{RZ6G3D} and use your PDX email and PDX Student ID and your given name and family name.
Turning this assignment in via gradescope will be confirmation that you have completed this problem.

Be sure to follow the instructions given \href{http://gradescope-static-assets.s3-us-west-2.amazonaws.com/help/submitting_hw_guide.pdf}{here} and link all problems to get credit.

\section{\normalfont{(0 pts)}}

Join the course slack channel following the link \href{https://join.slack.com/t/ee516-mfml-2023/shared_invite/zt-1miv4eyd1-v9C4Q7AuqvyRKAoXeDxo7Q}{here} and introduce yourself in the \texttt{social} channel. Please state: (1) your name, (2) why you're taking this class, and (3) a boring fact about yourself (see my example). You must use your PDX email.

\section{\normalfont{(5 pts)}}

Spend a bit of time (no more than 30 minutes) researching what machine learning is. In the \texttt{interesting-reading} Slack channel, share either a definition that you found particularly helpful/clear or create and share your own definition based on what you've learned. Finally, explain why you think linear algebra and optimization may be important to machine learning.

\section{\normalfont{(0 pts)}}

Complete the skills inventory below. For each item listed, indicate your level of comfort among:
\vspace{2mm} \\
(a) never heard of it \qquad (b) heard of it \qquad (c) some experience \qquad (d) strong experience

\begin{enumerate}

    \item matrix rank:
    \item span (of set of vectors):
    \item linear independence:
    \item basis (for a space/subspace):
    \item orthogonal vectors:
    \item eigenvalue decomposition:
    \item singular value decomposition:
    \item Frobenius norm:
    \item Principal Component Analysis:
    \item Spectral Clustering:
    \item Multidimensional Scaling:
    \item gradient descent:
    \item Alternating Direction Method of Multipliers (ADMM):
    \item Iteratively Reweighted Least Squares (IRLS):
    \item Empirical risk minimization

\end{enumerate}

\section{\normalfont{(0 pts)}}

Write (or type) something about yourself on a second page. This is just to test turning in assignments with solutions on different pages.

\end{document}
