Update course description, grading rubrics, and calendar for Fall 2025 schedule

Author: Liby99

index.html

@@ -21,37 +21,68 @@ <h1>Machine Programming</h1>
         <section>
             <h2>Course Information</h2>
             <ul class="info-list">
-                <li><strong>Instructor:</strong> [Instructor Name]</li>
-                <li><strong>Email:</strong> [Instructor Email]</li>
-                <li><strong>Time:</strong> [Class Time]</li>
-                <li><strong>Location:</strong> [Class Location]</li>
-                <li><strong>Office Hours:</strong> [Office Hours Info]</li>
+                <li><strong>Instructor:</strong> Ziyang Li</li>
+                <li><strong>Email:</strong> ziyang@cs.jhu.edu</li>
+                <li><strong>Time:</strong> Tuesday and Thursday 12:00pm - 1:15pm</li>
+                <li><strong>Location:</strong> TBD</li>
+                <li><strong>Office Hours:</strong> TBD</li>
             </ul>
         </section>
+        <section>
+            <h2>Course Description</h2>
+            <p>Programs are the fundamental medium through which humans interact with computers.
+                With the advent of large language models (LLMs), the automated synthesis of programs is rapidly transforming how we build software.
+                Instead of manual code writing, we specify intent through examples, specifications, and natural language.
+            </p>
+            <p>
+                This course explores both the foundations and frontiers of program synthesis, covering traditional symbolic techniques alongside LLM-driven approaches.
+                Students will study a variety of synthesis paradigms, including example-based, type- and specification-guided, and interactive methods.
+                We will examine how LLMs are applied to general-purpose programming tasks as well as to specialized domains such as theorem proving, program repair, planning, and verification.
+            </p>
+            <p>
+                Throughout the course, students will gain exposure to a wide range of programming languages, from widely-used ones like Python and C, to emerging and domain-specific languages such as Rust, Lean, CodeQL, and PDDL.
+                The course offers a research-oriented perspective combined with hands-on assignments and projects, providing students with both conceptual understanding and practical experience at the intersection of programming languages and machine learning.
+            </p>
+        </section>
+        <section>
+            <h2>Grading Rubrics</h2>
+            <p>
+                Students will be evaluated based on participation, assignments, a presentation, and a final project.
+                Active engagement throughout the course is strongly encouraged, both in class discussions and in peer feedback.
+                Exceptional oral presentation or final project may be rewarded with extra credit.
+            </p>
+            <ol>
+                <li>(20%) Class participation and active discussion</li>
+                <li>(15%) Oral presentation</li>
+                <li>(10%) Assignment 1: Traditional program synthesis</li>
+                <li>(10%) Assignment 2: Evaluating LLMs on program synthesis</li>
+                <li>(10%) Assignment 3: Iterative program synthesis</li>
+                <li>(35%) Final Project including proposal, report, and presentation</li>
+            </ol>
+        </section>
         <section id="calendar">
             <h2>Course Calendar</h2>
             <table class="calendar">
                 <tr>
                     <th>Week</th>
-                    <th>Date</th>
+                    <th>Dates</th>
                     <th>Topic / Event</th>
                 </tr>
-                <tr>
-                    <td>1</td>
-                    <td>Aug 27</td>
-                    <td>Introduction & Syllabus</td>
-                </tr>
-                <tr>
-                    <td>2</td>
-                    <td>Sep 3</td>
-                    <td>Foundations of Machine Programming</td>
-                </tr>
-                <tr>
-                    <td>3</td>
-                    <td>Sep 10</td>
-                    <td>Assignment 1 Released</td>
-                </tr>
-                <!-- Add more weeks/events as needed -->
+                <tr><td>1</td><td>Aug 26, Aug 28</td><td>Introduction & Syllabus</td></tr>
+                <tr><td>2</td><td>Sep 2, Sep 4</td><td>Lecture</td></tr>
+                <tr><td>3</td><td>Sep 9, Sep 11</td><td>Lecture</td></tr>
+                <tr><td>4</td><td>Sep 16, Sep 18</td><td>Assignment 1 Released</td></tr>
+                <tr><td>5</td><td>Sep 23, Sep 25</td><td>Lecture</td></tr>
+                <tr><td>6</td><td>Sep 30, Oct 2</td><td>Lecture</td></tr>
+                <tr><td>7</td><td>Oct 7, Oct 9</td><td>Assignment 1 Due / Assignment 2 Released</td></tr>
+                <tr><td>8</td><td>Oct 14, Oct 16</td><td>Lecture</td></tr>
+                <tr><td>9</td><td>Oct 21, Oct 23</td><td>Lecture</td></tr>
+                <tr><td>10</td><td>Oct 28, Oct 30</td><td>Assignment 2 Due / Assignment 3 Released</td></tr>
+                <tr><td>11</td><td>Nov 4, Nov 6</td><td>Lecture</td></tr>
+                <tr><td>12</td><td>Nov 11, Nov 13</td><td>Lecture</td></tr>
+                <tr><td>13</td><td>Nov 18, Nov 20</td><td>Assignment 3 Due / Project Proposal Due</td></tr>
+                <tr><td>14</td><td>Nov 25, Nov 27</td><td>Lecture (Nov 27: Thanksgiving, no class)</td></tr>
+                <tr><td>15</td><td>Dec 2, Dec 4</td><td>Final Project Presentations</td></tr>
             </table>
         </section>
     </main>