https://www.shaorongwang.com/tags/course/CourseHugoBlox Kit (https://hugoblox.com)en-USSun, 01 Feb 2026 00:00:00 +0000https://www.shaorongwang.com/media/icon_hu_eee4a95885829ab2.pnghttps://www.shaorongwang.com/tags/course/https://www.shaorongwang.com/courses/cpp-programming/Sun, 01 Feb 2026 00:00:00 +0000https://www.shaorongwang.com/courses/cpp-programming/<p>A course in C++ programming. The reference textbook is Stephen Prata, <em>C++ Primer Plus</em>, 6th edition, Posts &amp; Telecom Press, 2020.</p> <h2 id="course-information">Course Information</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="course-setup-and-source-code">Course Setup and Source Code</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="c-and-c-foundations">C and C++ Foundations</h2> <table> <thead> <tr> <th>Chapter</th> <th>Topic</th> <th>Files</th> </tr> </thead> <tbody> <tr> <td>01</td> <td>Getting Started with C++</td> <td> </td> </tr> <tr> <td>02</td> <td>Setting Out to C++</td> <td> </td> </tr> <tr> <td>03</td> <td>Dealing with Data</td> <td> </td> </tr> <tr> <td>04</td> <td>Compound Types</td> <td> </td> </tr> <tr> <td>05</td> <td>Loops and Relational Expressions</td> <td> </td> </tr> <tr> <td>06</td> <td>Branching Statements and Logical Operators</td> <td> </td> </tr> <tr> <td>07</td> <td>Functions: C++&rsquo;s Programming Modules</td> <td> </td> </tr> <tr> <td>08</td> <td>Adventures in Functions</td> <td> · </td> </tr> <tr> <td>09</td> <td>Memory Models and Namespaces</td> <td> · </td> </tr> </tbody> </table> <h2 id="object-oriented-programming-and-the-standard-library">Object-Oriented Programming and the Standard Library</h2> <table> <thead> <tr> <th>Chapter</th> <th>Topic</th> <th>Files</th> </tr> </thead> <tbody> <tr> <td>10</td> <td>Objects and Classes</td> <td> · · </td> </tr> <tr> <td>11</td> <td>Working with Classes</td> <td> · </td> </tr> <tr> <td>12</td> <td>Classes and Dynamic Memory Allocation</td> <td> · </td> </tr> <tr> <td>13</td> <td>Class Inheritance</td> <td> · </td> </tr> <tr> <td>14</td> <td>Reusing Code in C++</td> <td> · </td> </tr> <tr> <td>15</td> <td>Friends, Exceptions, and More</td> <td> · </td> </tr> <tr> <td>16</td> <td>The <code>string</code> Class and the Standard Template Library</td> <td> · </td> </tr> <tr> <td>17</td> <td>Input, Output, and Files</td> <td> · </td> </tr> </tbody> </table> <h2 id="projects-and-demonstrations">Projects and Demonstrations</h2> <ul> <li> </li> <li> </li> <li> </li> <li> · </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="exercises-and-online-judge">Exercises and Online Judge</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li>Solutions: · · · · · </li> </ul> <h2 id="development-environment-and-style">Development Environment and Style</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <p> </p>https://www.shaorongwang.com/courses/data-visualization/Sun, 01 Feb 2026 00:00:00 +0000https://www.shaorongwang.com/courses/data-visualization/<p>Beginning with perception and cognition, this course introduces fundamental methods in scientific visualization, information visualization, and cross-media data visualization. Assessment consists of exercises, projects, and paper presentations.</p> <h2 id="course-information">Course Information</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="lecture-slides">Lecture Slides</h2> <table> <thead> <tr> <th>Unit</th> <th>Topic</th> <th>Files</th> </tr> </thead> <tbody> <tr> <td>01</td> <td>Introduction to Data Visualization</td> <td> </td> </tr> <tr> <td>02</td> <td>Perception and Cognition</td> <td> </td> </tr> <tr> <td>03</td> <td>Data Foundations</td> <td> </td> </tr> <tr> <td>04</td> <td>Fundamentals of Data Visualization</td> <td> </td> </tr> <tr> <td>05</td> <td>Spatial Scalar Field Visualization</td> <td> </td> </tr> <tr> <td>06</td> <td>Large-Scale Multivariate Spatial Data Visualization</td> <td> </td> </tr> <tr> <td>07</td> <td>Time-Varying Data Visualization</td> <td> </td> </tr> <tr> <td>08</td> <td>Hierarchical and Network Data Visualization</td> <td> </td> </tr> <tr> <td>09</td> <td>Text and Document Visualization</td> <td> </td> </tr> <tr> <td>10</td> <td>Cross-Media Data Visualization</td> <td> </td> </tr> <tr> <td>10B</td> <td>Geospatial Data Visualization</td> <td> </td> </tr> <tr> <td>11</td> <td>Complex High-Dimensional Multivariate Data Visualization</td> <td> </td> </tr> </tbody> </table> <h2 id="exercises-and-project">Exercises and Project</h2> <ul> <li> </li> <li> </li> <li> </li> </ul> <h2 id="paper-presentation-resources">Paper Presentation Resources</h2> <ul> <li> </li> <li> </li> <li> </li> </ul> <p> </p>https://www.shaorongwang.com/courses/c-programming/Mon, 01 Sep 2025 00:00:00 +0000https://www.shaorongwang.com/courses/c-programming/<p>An introductory course in C programming. The reference textbook is Stephen Prata, <em>C Primer Plus</em>, 6th edition, Chinese edition, Posts &amp; Telecom Press, 2019.</p> <h2 id="course-information">Course Information</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="lecture-slides-and-source-code">Lecture Slides and Source Code</h2> <table> <thead> <tr> <th>Chapter</th> <th>Topic</th> <th>Files</th> </tr> </thead> <tbody> <tr> <td>01</td> <td>Getting Ready</td> <td> </td> </tr> <tr> <td>02</td> <td>Introducing C</td> <td> </td> </tr> <tr> <td>03</td> <td>Data and C</td> <td> </td> </tr> <tr> <td>04</td> <td>Character Strings and Formatted Input/Output</td> <td> </td> </tr> <tr> <td>05</td> <td>Operators, Expressions, and Statements</td> <td> </td> </tr> <tr> <td>06</td> <td>C Control Statements: Looping</td> <td> </td> </tr> <tr> <td>07</td> <td>C Control Statements: Branching and Jumps</td> <td> </td> </tr> <tr> <td>09</td> <td>Functions</td> <td> </td> </tr> <tr> <td>10</td> <td>Arrays and Pointers</td> <td> </td> </tr> <tr> <td>11</td> <td>Character Strings and String Functions</td> <td> </td> </tr> <tr> <td>13</td> <td>File Input/Output</td> <td> </td> </tr> <tr> <td>14</td> <td>Structures and Other Data Forms</td> <td> </td> </tr> <tr> <td>16</td> <td>The C Preprocessor and the C Library</td> <td> </td> </tr> <tr> <td>17</td> <td>Advanced Data Representation</td> <td> </td> </tr> </tbody> </table> <p>Textbook source packages:</p> <ul> <li> </li> <li> </li> <li> </li> </ul> <h2 id="exercises-and-assignments">Exercises and Assignments</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="development-resources">Development Resources</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="programming-practice-and-style">Programming Practice and Style</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <p> </p>https://www.shaorongwang.com/courses/machine-learning/Mon, 01 Sep 2025 00:00:00 +0000https://www.shaorongwang.com/courses/machine-learning/<p>A 40-hour elective for first-year graduate students, designed to establish a foundation for research in pattern recognition and machine learning. The course uses Python.</p> <h2 id="course-information">Course Information</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="statistical-learning">Statistical Learning</h2> <table> <thead> <tr> <th>Unit</th> <th>Topic</th> <th>Files</th> </tr> </thead> <tbody> <tr> <td>01</td> <td>Machine Learning and Statistical Learning</td> <td> </td> </tr> <tr> <td>02</td> <td>Perceptron</td> <td> </td> </tr> <tr> <td>03</td> <td>k-Nearest Neighbors</td> <td> </td> </tr> <tr> <td>04</td> <td>Bayesian Classifiers</td> <td> </td> </tr> <tr> <td>06</td> <td>Logistic Regression and Maximum Entropy</td> <td> </td> </tr> <tr> <td>07</td> <td>Support Vector Machines and Kernels</td> <td> </td> </tr> <tr> <td>09</td> <td>Expectation-Maximization</td> <td> · · </td> </tr> <tr> <td>13</td> <td>Unsupervised Learning</td> <td> </td> </tr> <tr> <td>14</td> <td>Clustering</td> <td> </td> </tr> <tr> <td>15</td> <td>Singular Value Decomposition</td> <td> · </td> </tr> <tr> <td>16</td> <td>Principal Component Analysis</td> <td> </td> </tr> <tr> <td>Appendix</td> <td>Distribution Measures</td> <td> </td> </tr> </tbody> </table> <p>Supplementary materials:</p> <ul> <li> </li> <li> </li> <li> </li> </ul> <h2 id="deep-learning">Deep Learning</h2> <p>These lectures refer to the and its .</p> <table> <thead> <tr> <th>Unit</th> <th>Topic</th> <th>Files</th> </tr> </thead> <tbody> <tr> <td>01</td> <td>Overview, Linear Algebra, and NDArray</td> <td> </td> </tr> <tr> <td>03</td> <td>Derivatives, Backpropagation, and Complexity</td> <td> </td> </tr> <tr> <td>04</td> <td>Linear Methods and Optimization</td> <td> </td> </tr> <tr> <td>05</td> <td>Maximum Likelihood and Logistic Regression</td> <td> </td> </tr> <tr> <td>06</td> <td>Multilayer Perceptrons</td> <td> </td> </tr> <tr> <td>08</td> <td>Numerical Stability, Activations, and Hardware</td> <td> </td> </tr> <tr> <td>11</td> <td>Convolution and Pooling</td> <td> </td> </tr> <tr> <td>12</td> <td>LeNet, AlexNet, VGG, and NiN</td> <td> </td> </tr> <tr> <td>13</td> <td>Inception, Batch Normalization, and Residual Networks</td> <td> </td> </tr> <tr> <td>15</td> <td>Image Augmentation, Fine-Tuning, and Style Transfer</td> <td> </td> </tr> <tr> <td>16</td> <td>Object Detection and Training Techniques</td> <td> </td> </tr> <tr> <td>18</td> <td>Sequence Models</td> <td> </td> </tr> <tr> <td>19</td> <td>Recurrent Neural Networks</td> <td> </td> </tr> <tr> <td>20</td> <td>Advanced Recurrent Neural Networks</td> <td> </td> </tr> <tr> <td>24</td> <td>Attention and Vision Transformers</td> <td> · </td> </tr> <tr> <td>25</td> <td>Optimization</td> <td> </td> </tr> </tbody> </table> <h2 id="language-models">Language Models</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="generative-models">Generative Models</h2> <ul> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> <li> </li> </ul> <h2 id="3d-vision">3D Vision</h2> <p>Covered topics include ray tracing of volume densities, EWA volume splatting, NeRF, and 3D Gaussian Splatting.</p> <h2 id="assignments">Assignments</h2> <ul> <li> </li> <li> </li> </ul> <h2 id="references">References</h2> <ul> <li>Hang Li, <em>Statistical Learning Methods</em>, 2nd edition, Tsinghua University Press, 2019.</li> <li>Hang Li, <em>Machine Learning Methods</em>, Tsinghua University Press, 2022.</li> <li>Aston Zhang et al., <em>Dive into Deep Learning</em>, PyTorch edition, Posts &amp; Telecom Press, 2023.</li> <li>Koki Saitoh, <em>Deep Learning from Scratch</em>, Posts &amp; Telecom Press, 2018.</li> </ul> <p> </p>