course_practicalities.tex

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          /Title      (Bayesian data analysis)
          /Author     (Aki Vehtari) %
          /Keywords   (Bayesian probability theory, Bayesian inference, Bayesian data analysis)
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\title[]{Bayesian data analysis}
\subtitle{Practical matters}

\author{Aki Vehtari}

\institute[Aalto University]{}

\begin{document}

\section{Course contents}


\begin{frame}
  \frametitle{Bayesian data analysis (Aalto fall 2019)}  %
  \framesubtitle{}
  \begin{itemize}
  \item Book: Gelman, Carlin, Stern, Dunson, Vehtari \& Rubin: Bayesian Data
    Analysis, Third Edition.
  \item Timetable: Lectures on Mondays at 14-16, TAs available See Oodi for exercise sessions
    \begin{itemize}
    \item TAs: Paul Bürkner, Alejandro Catalina, Akash Dhaka, Kunal
      Ghosh, Mäns Magnusson, Topi Paananen, Eero Siivola, Tuomas
      Sivula, Teemu Säilynoja
    % \item Oodi mentions TA session this week on Wednesday, but that
    %   has cancelled.
    \end{itemize}
  \end{itemize}
 \begin{center}
   \includegraphics[width=2.6cm]{figs/BDA3.jpg}
 \end{center}

\end{frame}

\begin{frame}
  \frametitle{Bayesian data analysis}  %
  \framesubtitle{Pre-requisites}
  \begin{itemize}
  \item Basic terms of probability theory
    \begin{itemize}
    \item probability, probability density, distribution
    \item sum, product rule, and Bayes' rule
    \item expectation, mean, variance, median
    \end{itemize}
  \item Some algebra and calculus
  \item Basic visualisation techniques (R or Python)
    \begin{itemize}
    \item histogram, density plot, scatter plot
    \end{itemize}
  \end{itemize}

  These will be tested with the first assignment round

\end{frame}

\begin{frame}
  \frametitle{Bayesian data analysis}  %
  \framesubtitle{Course contents}
  \begin{itemize}
  \item Background (Ch 1)
  \item Single-parameter models (Ch 2)
  \item Multiparameter models (Ch 3)
  \item Computational methods (Ch 10)
  \item Markov chain Monte Carlo (Ch 11--12)
  \item Stan and probabilistic programming
  \item Hierarchical models (Ch 5)
  \item Model checking (Ch 6)
  \item Evaluating and comparing models (Ch 7)
  \item Decision analysis (Ch 9)
  \item Large sample properties and Laplace approximation (Ch 4)
  \item In addition you learn workflow for Bayesian data analysis
  \end{itemize}
  
\end{frame}

\begin{frame}
  \frametitle{Bayesian data analysis}  %
  \framesubtitle{Example analyses}
  \begin{itemize}
  \item Treatment/control
    \begin{itemize}
    \item randomize patients to treatment or control
    \item is the treatment effective?
    \end{itemize}
    \pause
  \item Continuous valued treatment
    \begin{itemize}
    \item randomize patients with different dosages
    \item which dosage is sufficient without too many side effects?
    \end{itemize}
    \pause
  \item Different effects for different patients?
    \begin{itemize}
    \item Is the treatment effect different for male/female, child/adult, light/heavy, ...
    \end{itemize}
  \end{itemize}

\end{frame}

\begin{frame}
  \frametitle{Bayesian data analysis}  %
  \framesubtitle{Computer exercises}
  \begin{itemize}
  \item Basic visualisation techniques
  \item Binomial distribution -- Algae
  \item Normal distribution -- Windshield
  \item Difference between binomials -- Treatment/control
  \item Difference between normals -- Windshield
  \item Generalized linear model (GLM) + importance sampling -- Bioassay
  \item GLM + Metropolis + convergence diagnostics -- Bioassay
  \item GLM + Bioassay + Stan
  \item Linear model + Stan
  \item Hierarchical model + Stan
  \item Model seletion + Stan
  \end{itemize}

\end{frame}

\begin{frame}
  \frametitle{Bayesian data analysis}  %
  \framesubtitle{Assessment}
  \begin{itemize}
  \item Exercises (48p), and project work and presentation (24p)
     \begin{itemize}
     \item Minimum of 50\% of points must be obtained from both the project work and the exercises.
     % \item Preliminary grade boundaries\\
     %   <50\%=0, 50\%-60\%=1, 60\%-70\%=2, 70\%-80\%=3, 80\%-90\%=4, >90\%=5
     \end{itemize}
  \end{itemize}

\end{frame}

\begin{frame}
  \frametitle{Bayesian data analysis}  %

  \begin{itemize}
  \item Lectures describe basics and give broader overview
    \begin{itemize}
    \item part of lecture time for questions
    \item written material has all the details and self-study
      is possible
    \end{itemize}
  \item Supporting material, assignments and news in MyCourses
  \item Supporting material and assignments in \url{https://github.com/avehtari/BDA_course_Aalto}
    \begin{itemize}
    \item reading instructions and chapter notes
    \item demos
    \item slides (will be updated just before each lecture)
    \item video clips
    \item links to additional material
    \end{itemize}
  \item R demos \url{https://github.com/avehtari/BDA_R_demos/}
  \item (Python demos \url{https://github.com/avehtari/BDA_py_demos/})
  \item Slack channel
  \end{itemize}
  
\end{frame}

\begin{frame}
  \frametitle{Bayesian data analysis}  %
  \framesubtitle{Exercises}
  \begin{itemize}
  \item Weekly exercises introduced on Monday lecture
    \begin{itemize}
    \item R (Python) simulation exercises
    \item Stan probabilistic programming exercises (via R (Python))
    \end{itemize}
  \item Related R (Python) demos available
  \item TAs available: see Oodi for exercise sessions
  \item Exercise deadlines on Sunday (see detailed info in MyCourses)
  \item After exercise deadline grading period Monday--Tuesday
  \item Students grade 3 other exercises using peergrade.io
  \end{itemize}
  
\end{frame}

\begin{frame}
  \frametitle{Bayesian data analysis}  %
  \framesubtitle{R vs Python}

  \begin{itemize}
  \item We strongly recommend using R in the course as there are more
    packages for Stan and statistical analysis in general in R
  \item If you are already fluent in Python, but not in R, then using Python
    may be easier, but it can still be more useful to learn also R
  \end{itemize}
  
\end{frame}

\begin{frame}
  \frametitle{Bayesian data analysis}  %
  \framesubtitle{Exercises}
  \begin{itemize}
  \item Exercises are given on PeerGrade (also available in git repo)
  \item Exercises are returned and graded on Peergrade
  \end{itemize}
\end{frame}

\begin{frame}
  \frametitle{Exercises}  %
  \framesubtitle{peergrade.io}
  \begin{itemize}
  \item Used in BDA course since 2016
  \item Each student grades 3 exercises (randomly distributed)
  \item Detailed grading instructions -- rubric
  \item Also text feedback
  \item Possible to flag inappropriate grading
  \item TAs check flagged gradings
  \item Possible to give thumb up for great feedback
    \begin{itemize}
    \item those who give good feedback will get bonus points
    \end{itemize}
  \end{itemize}
  
\end{frame}

\begin{frame}
  \frametitle{Exercises}  %
  \framesubtitle{peergrade.io}

  \begin{itemize}
  \item Combined score: 70\% submission performance, 30\% feedback performance
    \pause
  \item Hand-in score:
    \begin{itemize}
    \item averaging the scores from peers
    \item after flagging teacher may overrule the score
    \item different exercises have different weight
    \end{itemize}
    See details at \url{http://help.peergrade.io/interfaces-and-features/grading-and-scores/the-hand-in-score}
    \pause
  \item Feedback score:
    \begin{itemize}
    \item The constructive score
    \item The hand-in evaluation accuracy score
    \item The feedback evaluation accuracy score
    \item The feedback completeness score
    \item The feedback evaluation completeness score
    \end{itemize}
    See details at \url{http://help.peergrade.io/interfaces-and-features/grading-and-scores/the-feedback-score}
  \end{itemize}
  
\end{frame}

\begin{frame}
  \frametitle{Peergrade.io}  %
  \framesubtitle{Registration}
  \begin{itemize}
  \item Go to peergrade.io/join
  \item Use class code: (see MyCourses announcements)
  \item Use your Aalto email or we can't match you to your student id
    % \begin{itemize}
    % \item Aalto is getting campus license and integration to Oodi, but
    %   lawyers are still checking data protection issues
    % \end{itemize}
  \end{itemize}
  
\end{frame}

\begin{frame}
  \frametitle{Project work}  %
  \framesubtitle{}
  \begin{itemize}
  \item Project work in groups of 1--3
    \begin{itemize}
    \item combines all the pieces learned in one project work
    \item R or Python notebook report
    \item project report peer graded
    \item oral presentation graded by me and TAs
    \end{itemize}
  \end{itemize}
  
\end{frame}


\end{document}

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