PageSourceSearch

https://itszhen.com/courses/dda4220-fall26/

html itszhen.com collected 2026-10-03 23:18:58 UTC 21,635 bytes, 180 lines download raw bytes

1<!DOCTYPE html>
2<html lang="en">
3<head>
4  <meta charset="utf-8">
5  <meta name="viewport" content="width=device-width, initial-scale=1">
6  <meta name="description" content="DDA4220: Deep Learning and Applications, Fall 2026, taught by Zhen Liu at CUHK-Shenzhen. Course schedule, materials, assessment, and teaching staff.">
7  <meta name="theme-color" content="#245a9b">
8  <title>DDA4220 · Deep Learning and Applications · Fall 2026</title>
9  <link rel="icon" href="favicon.svg" type="image/svg+xml">
10  <link rel="stylesheet" href="course.css">
11  
11<script src="course.js" defer></script>
11
12</head>
13<body>
14  <a class="skip-link" href="#main">Skip to course content</a>
15  <header class="masthead">
16    <div class="masthead-inner">
17      <a class="course-mark" href="../../index.html">Zhen Liu <span class="mark-divider" aria-hidden="true">/</span> Teaching</a>
18      <span class="institution">CUHK-Shenzhen <span class="institution-divider" aria-hidden="true">·</span> School of Data Science</span>
19    </div>
20  </header>
21
22  <main id="main">
23    <section class="hero page-width" id="top" aria-labelledby="course-title">
24      <div class="hero-copy">
25        <p class="eyebrow">DDA4220 <span aria-hidden="true">·</span> Fall 2026</p>
26        <h1 id="course-title">Deep Learning and Applications</h1>
27      </div>
28    </section>
29
30    <div class="course-strip">
31      <dl class="page-width quick-facts">
32        <div><dt>Lectures</dt><dd>Tue &amp; Thu · 10:30–12:00</dd></div>
33        <div><dt>Room</dt><dd>J_E205</dd></div>
34        <div><dt>First lecture</dt><dd><time datetime="2026-09-08">Tuesday, September 8</time></dd></div>
35      </dl>
36    </div>
37
38    <div class="page-width course-layout">
39      <aside class="section-index">
40        <nav aria-label="Course sections">
41          <a class="is-active" href="#overview" aria-current="location">Overview</a>
42          <a href="#schedule">Schedule</a>
43          <a href="#materials">Reading</a>
44          <a href="#information">Assessment &amp; policies</a>
45          <a href="#staff">Teaching team</a>
46        </nav>
47      </aside>
48
49      <div class="course-content">
50        <section class="content-section overview-section" id="overview" aria-labelledby="overview-title">
51          <div class="section-heading"><h2 id="overview-title">About the course</h2></div>
52          <p>This course introduces the foundations of deep learning: neural network architectures, training objectives, optimization, regularization, and the engineering practices behind effective models. We explore supervised, self-supervised, unsupervised, and reinforcement learning, with applications in computer vision, natural language processing, and robotics.</p>
53          <p class="overview-followup">You will learn to design, implement, and train models with tools such as PyTorch and TensorFlow, gaining practical experience through hands-on assignments and projects. Advanced topics will be introduced as time permits.</p>
54          <details class="prerequisites"><summary>Prerequisites &amp; preparation <span aria-hidden="true">+</span></summary><div class="details-body"><p><strong>Python proficiency is required.</strong> The course prerequisites are:</p><ul class="prerequisite-codes"><li>CSC1001 / CSC1003</li><li>MAT2040 / MAT2041, or equivalent courses</li><li>DDA3020 / CSC4020 / DDA2020 / FTE4560</li></ul><p>Prior coursework or projects in computer vision or natural language processing, and familiarity with PyTorch or TensorFlow, are helpful. There are no co-requisites.</p></div></details>
55        </section>
56
57        <section class="content-section" id="schedule" aria-labelledby="schedule-title">
58          <div class="section-heading"><h2 id="schedule-title">Lecture schedule</h2></div>
59          <p class="schedule-note">The course schedule is tentative and subject to change. Lecture materials will be linked below as they become available.</p>
60          <div class="schedule-scroll" role="region" aria-label="Lecture schedule and deadlines" tabindex="0">
61          <table class="schedule-table">
62            <caption class="visually-hidden">Dated lecture plan for Fall 2026. October 1 and 6 have no class during the October 1–7 National Day holiday. The midterm is listed separately. Deadlines appear alongside lectures with their actual due dates stated explicitly. A dash in Materials means materials have not been posted, or are not applicable for holidays.</caption>
63            <thead><tr><th scope="col" class="lecture-column">Lecture</th><th scope="col" class="date-column">Date</th><th scope="col">Topics</th><th scope="col" class="schedule-status materials-column">Materials</th><th scope="col" class="deadlines-column">Deadlines</th></tr></thead>
64            <tbody>
65              <tr><th scope="row">01</th><td class="schedule-date"><time datetime="2026-09-08" aria-label="Tuesday, September 8, 2026">
65Sep 8</time></td><td><h3>Course introduction &amp; history</h3></td><td class="schedule-status"><a href="materials/lectures/L01.pdf" aria-label="Lecture 1 slides (PDF)">Slides (PDF)</a></td><td class="schedule-deadlines"></td></tr>
66              <tr><th scope="row">02</th><td class="schedule-date"><time datetime="2026-09-10" aria-label="Thursday, September 10, 2026">Sep 10</time></td><td><h3>Neural networks: representation and learning</h3></td><td class="schedule-status"><a href="materials/lectures/L02.pdf" aria-label="Lecture 2 slides (PDF)">Slides (PDF)</a></td><td class="schedule-deadlines"></td></tr>
67              <tr><th scope="row">03</th><td class="schedule-date"><time datetime="2026-09-15" aria-label="Tuesday, September 15, 2026">Sep 15</time></td><td><h3>Training neural networks I: backpropagation and optimizers</h3></td><td class="schedule-status"><a href="materials/lectures/L03.pdf" aria-label="Lecture 3 slides (PDF)">Slides (PDF)</a></td><td class="schedule-deadlines"></td></tr>
68              <tr><th scope="row">04</th><td class="schedule-date"><time datetime="2026-09-17" aria-label="Thursday, September 17, 2026">Sep 17</time></td><td><h3>Training neural networks II: initialization, normalization</h3></td><td class="schedule-status"><a href="materials/lectures/L04.pdf" aria-label="Lecture 4 slides (PDF)">Slides (PDF)</a></td><td class="schedule-deadlines"></td></tr>
69              <tr><th scope="row">05</th><td class="schedule-date"><time datetime="2026-09-22" aria-label="Tuesday, September 22, 2026">Sep 22</time></td><td><h3>Training neural networks III: generalization, regularization, and training practice</h3></td><td class="schedule-status"><a href="materials/lectures/L05.pdf" aria-label="Lecture 5 slides (PDF)">Slides (PDF)</a></td><td class="schedule-deadlines"></td></tr>
70              <tr><th scope="row">06</th><td class="schedule-date"><time datetime="2026-09-24" aria-label="Thursday, September 24, 2026">Sep 24</time></td><td><h3>Convolutional networks and object recognition</h3></td><td class="schedule-status"><a href="materials/lectures/L06.pdf" aria-label="Lecture 6 slides (PDF)">Slides (PDF)</a></td><td class="schedule-deadlines"></td></tr>
71              <tr><th scope="row">07</th><td class="schedule-date"><time datetime="2026-09-29" aria-label="Tuesday, September 29, 2026">Sep 29</time></td><td><h3>Visual representations and transfer learning</h3></td><td class="schedule-status"><a href="materials/lectures/L07.pdf" aria-label="Lecture 7 slides (PDF)">Slides (PDF)</a></td><td class="schedule-deadlines"></td></tr>
72              <tr class="holiday-row"><th scope="row"><span class="visually-hidden">No lecture</span>—</th><td class="schedule-date"><time datetime="2026-10-01" aria-label="Thursday, October 1, 2026">Oct 1</time></td><td><h3>National holiday — no class</h3></td><td class="schedule-status"><span aria-label="Not applicable">—</span></td><td class="schedule-deadlines"></td></tr>
73              <tr class="holiday-row"><th scope="row"><span class="visually-hidden">No lecture</span>—</th><td class="schedule-date"><time datetime="2026-10-06" aria-label="Tuesday, October 6, 2026">Oct 6</time></td><td><h3>National holiday — no class</h3></td><td class="schedule-status"><span aria-label="Not applicable">—</span></td><td class="schedule-deadlines"></td></tr>
74              <tr><th scope="row">08</th><td class="schedule-date"><time datetime="2026-10-08" aria-label="Thursday, October 8, 2026">Oct 8</time></td><td><h3>Dense prediction and segmentation</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
75              <tr><th scope="row">09</th><td class="schedule-date"><time datetime="2026-10-13" aria-label="Tuesday, October 13, 2026">Oct 13</time></td><td><h3>Object detection and instance segmentation</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
76              <tr><th scope="row">10</th><td class="schedule-date"><time datetime="2026-10-15" aria-label="Thursday, October 15, 2026">Oct 15</time></td><td><h3>Sequence models and language modeling</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"><a href="materials/assignments/A1_Student_Package.zip" download>Assignment 1 (due <time datetime="2026-10-16">Oct 16</time>)</a><br><a href="project.html#proposal">Proposal (due <time datetime="2026-10-16">Oct 16</time>)</a></td></tr>
77              <tr><th scope="row">11</th><td class="schedule-date"><time datetime="2026-10-20" aria-label="Tuesday, October 20, 2026">Oct 20</time></td><td><h3>Sequence-to-sequence models and attention</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
78              <tr><th scope="row">12</th><td class="schedule-date"><time datetime="2026-10-22" aria-label="Thursday, October 22, 2026">Oct 22</time></td><td><h3>Transformers and Vision Transformers</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
79              <tr><th scope="row">13</th><td class="schedule-date"><time datetime="2026-10-27" aria-label="Tuesday, October 27, 2026">Oct 27</time></td><td><h3>Self-supervised learning</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
80              <tr><th scope="row">14</th><td class="schedule-date"><time datetime="2026-10-29" aria-label="Thursday, October 29, 2026">Oct 29</time></td><td><h3>Autoencoders &amp; variational autoencoders</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
81              <tr><th scope="row">15</th><td class="schedule-date"><time datetime="2026-11-03" aria-label="Tuesday, November 3, 2026">Nov 3</time></td><td><h3>Midterm review</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
82              <tr class="exam-row" id="midterm-exam"><th scope="row"><span class="visually-hidden">Assessment</span>—</th><td class="schedule-date"><time datetime="2026-11-05" aria-label="Thursday, November 5, 2026">Nov 5</time></td><td><h3>Midterm exam (tentative)</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
83              <tr><th scope="row">16</th><td class="schedule-date"><time datetime="2026-11-10" aria-label="Tuesday, November 10, 2026">Nov 10</time></td><td><h3>Generative adversarial networks</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
84              <tr><th scope="row">17</th><td class="schedule-date"><time datetime="2026-11-12" aria-label="Thursday, November 12, 2026">Nov 12</time></td><td><h3>Diffusion models</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
85              <tr><th scope="row">18</th><td class="schedule-date"><time datetime="2026-11-17" aria-label="Tuesday, November 17, 2026">Nov 17</time></td><td><h3>Conditional generation and flow matching</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"><a href="project.html#midterm-report">Midterm report (due <time datetime="2026-11-18">Nov 18</time>)</a></td></tr>
86              <tr><th scope="row">19</th><td class="schedule-date"><time datetime="2026-11-19" aria-label="Thursday, November 19, 2026">Nov 19</time></td><td><h3>Reinforcement Learning: Foundations and Value-Based Methods</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
87              <tr><th scope="row">20</th><td class="schedule-date"><time datetime="2026-11-24" aria-label="Tuesday, November 24, 2026">Nov 24</time></td><td><h3>Reinforcement Learning: Policy Gradients and Actor–Critic Methods</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
88              <tr><th scope="row">21</th><td class="schedule-date"><time datetime="2026-11-26" aria-label="Thursday, November 26, 2026">Nov 26</time></td><td><h3>Language foundation models</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
89              <tr><th scope="row">22</th><td class="schedule-date"><time datetime="2026-12-01" aria-label="Tuesday, December 1, 2026">Dec 1</time></td><td><h3>Multimodal &amp; generative foundation models</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
90              <tr><th scope="row">23</th><td class="schedule-date"><time datetime="2026-12-03" aria-label="Thursday, December 3, 2026">Dec 3</time></td><td><h3>3D vision</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
91              <tr><th scope="row">24</th><td class="schedule-date"><time datetime="2026-12-08" aria-label="Tuesday, December 8, 2026">Dec 8</time></td><td><h3>Robot learning</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
92              <tr><th scope="row">25</th><td class="schedule-date"><time datetime="2026-12-10" aria-label="Thursday, December 10, 2026">Dec 10</time></td><td><h3>Advanced topics I</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
93              <tr><th scope="row">26</th><td class="schedule-date"><time datetime="2026-12-15" aria-label="Tuesday, December 15, 2026">Dec 15</time></td><td><h3>Advanced topics II</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
94              <tr><th scope="row">27</th><td class="schedule-date"><time datetime="2026-12-17" aria-label="Thursday, December 17, 2026">Dec 17</time></td><td><h3>Course review &amp; discussion</h3></td><td class="schedule-status"><span aria-label="Not yet posted">—</span></td><td class="schedule-deadlines"></td></tr>
95            </tbody>
96          </table>
97          </div>
98        </section>
99
100        <section class="content-section" id="materials" aria-labelledby="materials-title">
101          <div class="section-heading"><h2 id="materials-title">Recommended reading</h2></div>
102          <div class="reading-list">
103            <p>No textbook is required.</p>
104            <a class="book-link" href="https://www.deeplearningbook.org/"><div><h3>Deep Learning</h3><p>Ian Goodfellow, Yoshua Bengio &amp; Aaron Courville<span>MIT Press</span></p></div></a>
105            <a class="book-link" href="https://www.bishopbook.com/"><div><h3>Deep Learning: Foundations and Concepts</h3><p>Christopher M. Bishop &amp; Hugh Bishop<span>Springer</span></p></div></a>
106          </div>
107        </section>
108
109        <section class="content-section" id="information" aria-labelledby="information-title">
110          <div class="section-heading"><h2 id="information-title">Assessment &amp; policies</h2></div>
111          <dl class="assessment-list">
112            <div><dt>3 homework assignments</dt><dd>45%</dd></div>
113            <div><dt><a href="project.html">Final project</a><span>Project description, milestones, and requirements.</span></dt><dd>25%</dd></div>
114            <div><dt><a href="#midterm-exam">Midterm exam</a></dt><dd>30%</dd></div>
115          </dl>
116          <div class="assignment-section" id="assignments" aria-labelledby="assignments-title">
117            <h3 id="assignments-title">Assignments</h3>
118            <ul class="assignment-list">
119              <li><span class="assignment-details"><span class="assignment-title">Assignment 1</span><a class="assignment-download" href="materials/assignments/A1_Student_Package.zip" aria-label="Download Assignment 1 package (ZIP)" download>Package (ZIP)</a></span><span class="assignment-status">Due <time datetime="2026-10-16">Oct 16</time></span></li>
120              <li><span class="assignment-title">
120Assignment 2</span><span class="assignment-status">Not yet released</span></li>
121              <li><span class="assignment-title">Assignment 3</span><span class="assignment-status">Not yet released</span></li>
122            </ul>
123          </div>
124          <div class="policy-note"><h3>Course policies</h3><p>See <a href="materials/lectures/L01.pdf#page=85">L01</a> for coursework and AI-use guidance.</p><p><strong>Late submissions:</strong> You have <strong>3 grace days for all homework combined</strong>. Each 24-hour period, or part of one, uses 1 grace day: 23 hours late uses 1 day; 25 hours uses 2.</p></div>
125        </section>
126
127        <section class="content-section staff-section" id="staff" aria-labelledby="staff-title">
128          <div class="section-heading"><h2 id="staff-title">Teaching team &amp; office hours</h2></div>
129          <div class="staff-grid">
130            <div class="instructor"><div><p class="staff-role">Instructor</p><h3>Zhen Liu</h3><span class="staff-email">[first name + last name]@cuhk.edu.cn</span><a class="plain-link" href="../../index.html">Personal webpage</a></div></div>
131            <div class="staff-details" id="office-hours"><h3>Instructor office hours</h3><p class="office-time">Tuesday 09:00–10:00 · every other week</p><p>First session: <time datetime="2026-09-15">Tuesday, September 15, 2026</time>.</p><p>Daoyuan 323B (<span lang="zh-Hans">道远楼323B</span>)</p><p class="office-policy">Held only by request; please email at least one day in advance.</p></div>
132          </div>
133          <div class="ta-list" aria-label="Teaching assistants">
134            <div class="ta-person">
135              <p class="staff-role">Lead teaching assistant</p>
136              <h3>Wenqian Zhang <span lang="zh-Hans">张文谦</span></h3>
137              <a class="staff-email" href="mailto:[email protected]">[email protected]</a>
138              <div class="ta-office-hours">
139                <h4>Office hours</h4>
140                <p>Tuesday 17:00–18:00<br>Thursday 14:30–15:30</p>
141                <p class="ta-location">Zhixin (<span lang="zh-Hans">知新楼</span>) 408</p>
142              </div>
143            </div>
144            <div class="ta-person">
145              <p class="staff-role">Teaching assistant</p>
146              <h3>Ruixiang Wang <span lang="zh-Hans">王瑞翔</span></h3>
147              <a class="staff-email" href="mailto:[email protected]">[email protected]</a>
148              <div class="ta-office-hours">
149                <h4>Office hours</h4>
150                <p>Saturday 17:00–18:00<br>Sunday 17:00–18:00</p>
151                <p class="ta-location">Teaching Complex B 609 (<span lang="zh-Hans">教学综合楼B 609</span>)</p>
152              </div>
153            </div>
154            <div class="ta-person">
155              <p class="staff-role">Teaching assistant</p>
156              <h3>Yifan Peng <span lang="zh-Hans">彭一凡</span></h3>
157              <a class="staff-email" href="mailto:[email protected]">[email protected]</a>
158              <div class="ta-office-hours">
159                <h4>Office hours</h4>
160                <p>Monday 19:00–20:00<br>Wednesday 19:00–20:00</p>
161                <p class="ta-location">Zhixin (<span lang="zh-Hans">知新楼</span>) 429</p>
162              </div>
163            </div>
164          </div>
165        </section>
166        <section class="content-section acknowledgments" id="acknowledgments" aria-labelledby="acknowledgments-title">
167          <div class="section-heading"><h2 id="acknowledgments-title">Acknowledgments</h2></div>
168          <p>This course draws inspiration from <a href="https://cs231n.stanford.edu/">Stanford CS231n</a>, <a href="https://sites.cc.gatech.edu/classes/AY2025/cs7643_fall/">Georgia Tech CS 4644/7643 (Danfei Xu)</a>, <a href="https://deeplearning.cs.cmu.edu/F25/index.html">CMU 11-785</a>, and <a href="https://introtodeeplearning.com/">MIT 6.S191</a>. We also borrow a few figures from their teaching materials.</p>
169        </section>
170      </div>
171    </div>
172  </main>
173
174  <footer class="page-width footer">
175    <a class="footer-course" href="#top">DDA4220 <span>Deep Learning and Applications</span></a>
176    <span>Fall 2026 · CUHK-Shenzhen</span>
177    <a class="back-top" href="#top">Back to top <span aria-hidden="true">↑</span></a>
178  </footer>
179</body>
180</html>

Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.