Graduate course syllabus with project timeline (article)

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\title{\textbf{Your paper}\\[3pt]\large STAT 6310 -- Graduate Seminar in Statistical Learning}
\author{Instructor Name \quad Department of Statistics, University Name}
\date{Spring 2027 \quad 3 credit hours \quad Tuesday and Thursday, 2:00--3:15 pm, Room 000}

\begin{document}
\maketitle

\begin{tabularx}{\textwidth}{@{}lX@{}}
\toprule
Instructor        & Instructor Name, Office 0.000, \texttt{[email protected]} \\
Office hours      & Wednesday 10:00--11:30 am, or by appointment \\
Teaching assistant & Assistant Name; office hours Monday 3:00--4:30 pm \\
Prerequisites     & Graduate probability and linear regression, or permission of the instructor \\
Course website    & Learning management system (slides, problem sets, submissions) \\
\bottomrule
\end{tabularx}

\section*{Description}
A doctoral-level introduction to the theory and practice of statistical learning. We
study the bias--variance trade-off, regularised regression, kernel methods, trees and
ensembles, and the generalisation bounds that explain when these methods work. Lectures
alternate with student-led paper discussions. A central quantity throughout is the
expected prediction error of an estimator $\hat f$ at a point $x_0$,
\begin{equation}
  \operatorname{E}\bigl[(Y-\hat f(x_0))^2\bigr]
  = \sigma^2 + \operatorname{Bias}^2\bigl(\hat f(x_0)\bigr) + \operatorname{Var}\bigl(\hat f(x_0)\bigr).
\end{equation}

\section*{Learning outcomes}
Students who complete the course will be able to:
\begin{enumerate}[label=(\arabic*)]
  \item derive the estimators and risk properties of the main learning methods;
  \item choose and tune a method for a data set using resampling;
  \item read a current methods paper critically and present it to peers;
  \item carry out and write up an original analysis at the standard of a conference paper.
\end{enumerate}

\section*{Materials}
A standard graduate text on statistical learning (available free online from its
authors), journal articles posted on the course website, and a statistical computing
environment of your choice (R or Python). Problem sets are typeset in \LaTeX{}.

\section*{Schedule}
\begin{tabularx}{\textwidth}{@{}clX@{}}
\toprule
Week & Dates & Topic \\
\midrule
1  & Jan 12, 14 & Overview; the learning problem; loss and risk \\
2  & Jan 19, 21 & Linear methods and least squares revisited \\
3  & Jan 26, 28 & Ridge, lasso and the elastic net \\
4  & Feb 2, 4   & Model selection, cross-validation, the bootstrap \\
5  & Feb 9, 11  & Basis expansions and smoothing splines \\
6  & Feb 16, 18 & Kernel methods and reproducing kernel Hilbert spaces \\
7  & Feb 23, 25 & Support vector machines \\
8  & Mar 2, 4   & Trees, bagging and random forests \\
9  & Mar 9, 11  & Spring break (no class) \\
10 & Mar 16, 18 & Boosting \\
11 & Mar 23, 25 & Generalisation bounds and VC dimension \\
12 & Mar 30, Apr 1 & High-dimensional inference \\
13 & Apr 6, 8   & Neural networks from a statistical view \\
14 & Apr 13, 15 & Paper discussions \\
15 & Apr 20, 22 & Project presentations \\
\bottomrule
\end{tabularx}

\section*{Problem sets and project timeline}
Four problem sets (PS) are released on Tuesdays and due two weeks later. The project
runs in parallel, with three checkpoints before the final report.

\begin{center}
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  \node[below] at (8.5,-0.9) {Week of term};
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  \node[proj, left] at (4,-2) {Project};
  \node[ps, left] at (1.8,1.5) {Problem sets};
\end{tikzpicture}
\end{center}

\section*{Assessment}
\begin{tabularx}{\textwidth}{@{}Xr@{}}
\toprule
Component & Weight \\
\midrule
Problem sets (4, equally weighted)                    & 30\% \\
Paper presentation and discussion leadership          & 10\% \\
Participation in discussions                          & 5\% \\
Project proposal (2 pages)                            & 5\% \\
Project data and methods memo                         & 10\% \\
Project presentation                                  & 10\% \\
Final project report (8--10 pages, conference format) & 30\% \\
\midrule
Total                                                 & 100\% \\
\bottomrule
\end{tabularx}

\paragraph{Grading scale.}
A 93--100; A$-$ 90--92.9; B$+$ 87--89.9; B 83--86.9; B$-$ 80--82.9; C 70--79.9; F below 70.
Graduate credit requires a B$-$ or better.

\section*{Policies}
\begin{description}[style=unboxed, font=\bfseries]
  \item[Attendance.] This is a seminar; attendance at every session is expected. Let the
    instructor know in advance if you must miss a class.
  \item[Late work.] Each student has four late days for the semester, usable on problem
    sets only (at most two per set). After that, late problem sets receive no credit.
    Project deadlines are firm.
  \item[Academic integrity.] You may discuss problem sets with classmates, but you must
    write up your own solutions and code and list your collaborators. All sources used in
    the project must be cited. Suspected violations are referred to the graduate school
    under the university's academic integrity policy.
  \item[Accessibility.] Students who need accommodations should register with the
    disability resource office and send the accommodation letter to the instructor as
    early as possible.
\end{description}

\section*{Important dates}
\begin{tabular}{@{}ll@{}}
\toprule
First class                   & Tuesday, January 12, 2027 \\
Project proposal due          & Friday, February 12, 2027 \\
Spring break                  & March 8--12, 2027 \\
Data and methods memo due     & Friday, March 19, 2027 \\
Project draft for peer review & Friday, April 9, 2027 \\
Project presentations         & April 20 and 22, 2027 \\
Final report due              & Friday, April 30, 2027 \\
\bottomrule
\end{tabular}

\end{document}