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    <meta name="description" content="DATASCI 207: Applied Machine Learning - Course Materials">
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            <h1 class="overlay-title">DATASCI 207: Applied Machine Learning</h1>
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                <h2>Course Portal</h2>
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                <span class="site-title">DATASCI 207: Applied Machine Learning</span>
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        <main>
            <div class="container">
                <h1>Course Materials</h1>

                <p>
                    Welcome to DATASCI 207. This course covers the foundations of machine learning,
                    from linear models through deep learning architectures. Each module includes
                    lecture code, a glossary of key terms, self-check quizzes, and additional readings.
                </p>

                <h2>Modules</h2>

                <div class="week-grid">
                    <div class="week-card">
                        <a href="week_01/index.html">
                            <span class="week-number">Module 1</span>
                            <h3>Introduction and Framing</h3>
                            <p>Functions, models, generalization, overfitting, train/test split, baselines</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_02/index.html">
                            <span class="week-number">Module 2</span>
                            <h3>Linear Regression and Gradient Descent</h3>
                            <p>Loss functions, optimization, batch/stochastic/mini-batch gradient descent</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_03/index.html">
                            <span class="week-number">Module 3</span>
                            <h3>Feature Engineering</h3>
                            <p>Scaling, encoding, feature crosses, feature selection, curse of dimensionality</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_04/index.html">
                            <span class="week-number">Module 4</span>
                            <h3>Logistic Regression</h3>
                            <p>Sigmoid function, decision boundaries, classification thresholds, logistic loss</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_05/index.html">
                            <span class="week-number">Module 5</span>
                            <h3>Multiclass Classification and Metrics</h3>
                            <p>Softmax, cross-entropy, precision, recall, confusion matrices</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_06/index.html">
                            <span class="week-number">Module 6</span>
                            <h3>Feedforward Neural Networks</h3>
                            <p>XOR problem, hidden layers, activation functions, backpropagation, optimizers</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_07/index.html">
                            <span class="week-number">Module 7</span>
                            <h3>KNN, Decision Trees, and Ensembles</h3>
                            <p>Non-parametric models, entropy, information gain, random forests, boosting</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_08/index.html">
                            <span class="week-number">Module 8</span>
                            <h3>Unsupervised Learning: k-Means and PCA</h3>
                            <p>Clustering, hierarchical methods, GMM, dimensionality reduction, SVD, t-SNE</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_09/index.html">
                            <span class="week-number">Module 9</span>
                            <h3>Embeddings For Text</h3>
                            <p>Tokenization, one-hot, bag-of-words, word embeddings, transfer learning</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_10/index.html">
                            <span class="week-number">Module 10</span>
                            <h3>Convolutional Neural Networks</h3>
                            <p>Convolutional filters, pooling, stride, padding, ImageNet, transfer learning</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_11/index.html">
                            <span class="week-number">Module 11</span>
                            <h3>Network Architecture Design</h3>
                            <p>Keras Functional API, multi-input/output models, embedding layers, pipelines</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_12/index.html">
                            <span class="week-number">Module 12</span>
                            <h3>Fairness</h3>
                            <p>Bias, subgroup metrics, COMPAS case study, adversarial training, ethical AI</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="week_13/index.html">
                            <span class="week-number">Module 13</span>
                            <h3>Advanced Topics</h3>
                            <p>RNNs, seq2seq, attention mechanisms, Transformers, BERT, GPT, LLMs</p>
                        </a>
                    </div>
                </div>

                <h2>Resources</h2>

                <div class="week-grid">


                    <div class="week-card">
                        <a href="how_to_read_papers.html">
                            <span class="week-number">Guide</span>
                            <h3>How to Read ML Papers</h3>
                            <p>Strategies for reading and understanding research papers</p>
                        </a>
                    </div>


                    <div class="week-card">
                        <a href="capstone_best_practices.html">
                            <span class="week-number">Final Project</span>
                            <h3>Capstone Best Practices</h3>
                            <p>Recommendations for your final project</p>
                        </a>
                    </div>

                    <div class="week-card">
                        <a href="curated_resources.html">
                            <span class="week-number">External Links</span>
                            <h3>Curated Resources</h3>
                            <p>Tutorials, papers, videos, tools, and articles from across the web</p>
                        </a>
                    </div>
                </div>

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                    <p>DATASCI 207: Applied Machine Learning</p>
                </footer>
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