uni/year4/semester1/CT4101: Machine Learning/notes/CT4101-Notes.tex

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\author{Andrew Hayes}

\begin{document}
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        \censor{\huge \textbf{CT4101}}
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            Machine Learning
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            Name: Andrew Hayes \\
            E-mail: \href{mailto://a.hayes18@universityofgalway.ie}{\texttt{a.hayes18@universityofgalway.ie}}  \hfill\\   
            Student ID: 21321503 \hfill
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\section{Introduction}
\subsection{Lecturer Contact Details}
\begin{itemize}
    \item   Dr. Frank Glavin.
    \item   \href{mailto://frank.glavin@universityofgalway.ie}{\texttt{frank.glavin@universityofgalway.ie}}
\end{itemize}

\subsection{Grading}
\begin{itemize}
    \item   Continuous Assessment: 30\% (2 assignments, worth 15\% each).
    \item   Written Exam: 70\% (Last 2 year's exam papers most relevant).
\end{itemize}

\subsection{Module Overview}
\textbf{Machine Learning (ML)} allows computer programs to improve their performance with experience (i.e., data).
This module is targeted at learners with no prior ML experience, but with university experience of mathematics \& 
statistics and \textbf{strong} programming skills.
The focus of this module is on practical applications of commonly used ML algorithms, including deep learning 
applied to computer vision.
Students will learn to use modern ML frameworks (e.g., scikit-learn, Tensorflow / Keras) to train \& evaluate 
models for common categories of ML task including classification, clustering, \& regression.

\subsubsection{Learning Objectives}
On successful completion, a student should be able to:
\begin{enumerate}
    \item   Explain the details of commonly used Machine Learning algorithms.
    \item   Apply modern frameworks to develop models for common categories of Machine Learning task, including
            classification, clustering, \& regression.
    \item   Understand how Deep Learning can be applied to computer vision tasks.
    \item   Pre-process datasets for Machine Learning tasks using techniques such as normalisation \& feature 
            selection.
    \item   Select appropriate algorithms \& evaluation metrics for a given dataset \& task.
    \item   Choose appropriate hyperparameters for a range of Machine Learning algorithms.
    \item   Evaluate \& interpret the results produced by Machine Learning models.
    \item   Diagnose \& address commonly encountered problems with Machine Learning models.
    \item   Discuss ethical issues \& emerging trends in Machine Learning.
\end{enumerate}

\section{What is Machine Learning?}
There are many possible definitions for ``machine learning'':
\begin{itemize}
    \item   Samuel, 1959: ``Field of study that gives computers the ability to learn without being explicitly 
            programmed''.
    \item   Witten \& Frank, 1999: ``Learning is changing behaviour in a way that makes \textit{performance} better
            in the future''.
    \item   Mitchelll, 1997: ``Improvement with experience at some task''. 
            A well-defined ML problem will improve over task $T$ with regards to \textbf{performance} measure $P$,
            based on experience $E$.
    \item   Artificial Intelligence $\neq$ Machine Learning $\neq$ Deep Learning.
    \item   Artificial Intelligence $\not \supseteq$ Machine Learning $\not \supseteq$ Deep Learning.
\end{itemize}

Machine Learning techniques include:
\begin{itemize}
    \item   Supervised learning.
    \item   Unsupervised learning.
    \item   Semi-Supervised learning.
    \item   Reinforcement learning.
\end{itemize}

Major types of ML task include:
\begin{enumerate}
    \item   Classification.
    \item   Regression.
    \item   Clustering.
    \item   Co-Training.
    \item   Relationship discovery.
    \item   Reinforcement learning.
\end{enumerate}

Techniques for these tasks include:
\begin{enumerate}
    \item   \textbf{Supervised learning:}
            \begin{itemize}
                \item   \textbf{Classification:} decision trees, SVMs.
                \item   \textbf{Regression:} linear regression, neural nets, $k$-NN (good for classification too).
            \end{itemize}

    \item   \textbf{Unsupervised learning:}
            \begin{itemize}
                \item   \textbf{Clustering:} $k$-Means, EM-clustering.
                \item   \textbf{Relationship discovery:} association rules, bayesian nets.
            \end{itemize}

    \item   \textbf{Semi-Supervised learning:}
            \begin{itemize}
                \item   \textbf{Learning from part-labelled data:} co-training, transductive learning (combines ideas
                        from clustering \& classification).
            \end{itemize}

    \item   \textbf{Reward-Based:}
            \begin{itemize}
                \item   \textbf{Reinforcement learning:} Q-learning, SARSA.
            \end{itemize}
\end{enumerate}

In all cases, the machine searches for a \textbf{hypothesis} that best describes the data presented to it.
Choices to be made include:
\begin{itemize}
    \item   How is the hypothesis expressed? e.g., mathematical equation, logic rules, diagrammatic form, table, 
            parameters of a model (e.g. weights of an ANN), etc.
    \item   How is search carried out? e.g., systematic (breadth-first or depth-first) or heuristic (most promising
            first).
    \item   How do we measure the quality of a hypothesis?
    \item   What is an appropriate format for the data?
    \item   How much data is required?
\end{itemize}

To apply ML, we need to know:
\begin{itemize}
    \item   How to formulate a problem.
    \item   How to prepare the data.
    \item   How to select an appropriate algorithm.
    \item   How to interpret the results.
\end{itemize}

To evaluate results \& compare methods, we need to know:
\begin{itemize}
    \item   The separation between training, testing, \& validation.
    \item   Performance measures such as simple metrics, statistical tests, \& graphical methods.
    \item   How to improve performance.
    \item   Ensemble methods.
    \item   Theoretical bounds on performance.
\end{itemize}

\subsection{Data Mining}
\textbf{Data Mining} is the process of extracting interesting knowledge from large, unstructured datasets.
This knowledge is typically non-obvious, comprehensible, meaningful, \& useful.
\\\\
The storage ``law'' states that storage capacity doubles every year, faster than Moore's ``law'', which may results 
in write-only ``data tombs''. 
Therefore, developments in ML may be essential to be able to process \& exploit this lost data.

\subsection{Big Data}
\textbf{Big Data} consists of datasets of scale \& complexity such that they can be difficult to process using 
current standard methods.
The data scale dimensions are affected by one or more of the ``3 Vs'':
\begin{itemize}
    \item   \textbf{Volume:} terabytes \& up.
    \item   \textbf{Velocity:} from batch to streaming data.
    \item   \textbf{Variety:} numeric, video, sensor, unstructured text, etc.
\end{itemize}

It is also fashionable to add more ``Vs'' that are not key:
\begin{itemize}
    \item   \textbf{Veracity:} quality \& uncertainty associated with items.
    \item   \textbf{Variability:} change / inconsistency over time.
    \item   \textbf{Value:} for the organisation.
\end{itemize}

Key techniques for handling big data include: sampling, inductive learning, clustering, associations, \& distributed 
programming methods.





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