courseplan.html

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title: Advanced Tools for Data Analytics
subtitle: Course Plan
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      <p class="post-entry" style="padding:10px">Starting with an elementary introduction to statistics and probability, we will develop various regression, classification, dimensionlity reduction and advanced learning algorithms that are of interest to engineers. In addition, various widely-used machine learning software packages will be introduced. Registrants will solve exercises and receive take-away software code to implement these algorithms. The following is a general outline of the course:</p>
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        <b><li class="post-entry">Basics of probability and statistics, underfitting, overfitting and bias-variance tradeoff</b></li><br>

        <b><li class="post-entry">Classification Algorithms</b>
          <ul>
            <li class="post-entry"><i>k</i>-nearest neighbours algorithm</li>

            <li class="post-entry"><i>k</i>-means algorithm</li>

            <li class="post-entry">Support Vector Machines</li>

            <li class="post-entry">Naive Bayes Classifier and Decision Trees</li>
          </ul>
        </li><br>

        <b><li class="post-entry">Regression Algorithms</b>
          <ul>
            <li class="post-entry">Linear Least Squares</li>

            <li class="post-entry">Non-linear Least Squares</li>

            <li class="post-entry">Kernel Regression</li>
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        <b><li class="post-entry">
          Dimensionality Reduction Algorithms</b>
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            <li class="post-entry">Principal Component Analysis (PCA)</li>

            <li class="post-entry">Partial Least Squares (PLS)</li>

            <li class="post-entry">Isometric Mapping (ISOMAP)</li>

            <li class="post-entry">Local Linear Embedding (LLE)</li>

            <li class="post-entry">Canonical Correlation Analysis (CCA)</li>

            <li class="post-entry">Multidimensional Scaling (MDS)</li>
          </ul>
        </li><br>

        <b><li class="post-entry">Advanced Learning Algorithms</b>
          <ul>
            <li class="post-entry">Artificial Neural Networks</li>

            <li class="post-entry">Deep Learning</li>

            <li class="post-entry">Gaussian Processes</li>

            <li class="post-entry">Bayesian Networks</li>

            <li class="post-entry">Deep Reinforcement Learning</li>
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        </li><br>

        <b><li class="post-entry">Applications in the Process Industry</b></li><br>
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      <h2 class="post-title">Learning Outcomes</h2>
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      <p class="post-entry">By the end of this workshop, registrants will be able to:
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          <li>identify and solve classification, regression and dimensionality reduction problems</li>

          <li>work with softwares such as Python, TensorFlow, and Keras</li>
        </ul></p>
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