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schedule.html
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<!DOCTYPE html>
<html lang="en">
<head>
<!-- Meta Information -->
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="author" content="Yue Zhao">
<meta name="description" content="Course website of CSCI 566 AT USC">
<meta name="keywords" content="machine learning, data mining, csci566, usc, Yue Zhao">
<!-- Title and Favicon -->
<title>CSCI 566 Deep Learning and its Applications</title>
<link rel="icon" type="image/x-icon" href="images/logo2.ico">
<!-- Stylesheets -->
<link rel="stylesheet" href="https://unpkg.com/[email protected]/dist/css/bootstrap.min.css" integrity="sha384-xOolHFLEh07PJGoPkLv1IbcEPTNtaed2xpHsD9ESMhqIYd0nLMwNLD69Npy4HI+N" crossorigin="anonymous">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0-beta3/css/all.min.css">
<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap" rel="stylesheet">
<link href="https://fonts.googleapis.com/css2?family=Poppins:wght@700&display=swap" rel="stylesheet">
<!-- Scripts -->
<script src="https://code.jquery.com/jquery-3.6.0.slim.min.js" integrity="sha256-u7e5khyithlIdTpu22PHhENmPcRdFiHRjhAuHcs05RI=" crossorigin="anonymous"></script>
<script src="https://unpkg.com/[email protected]/dist/js/bootstrap.min.js" integrity="sha384-+sLIOodYLS7CIrQpBjl+C7nPvqq+FbNUBDunl/OZv93DB7Ln/533i8e/mZXLi/P+" crossorigin="anonymous"></script>
</head>
<body>
<style>
body {
font-family: 'Roboto', '-apple-system', 'BlinkMacSystemFont', 'Segoe UI', 'Helvetica Neue', 'Arial', 'Noto Sans', 'sans-serif', 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
}
/* Global Styles */
h1, h2, h3 {
text-align: center;
}
.university-color {
color: #990000;
}
.link-list a {
margin-right: 5px;
}
/* Staff Container Styles */
.staff-container {
display: flex;
justify-content: space-between;
align-items: center;
flex-wrap: wrap;
margin: 20px;
}
.staff-member {
width: 23%;
text-align: center;
margin: 15px 1%;
box-sizing: border-box;
}
.staff-member img {
width: 80%;
max-width: 150px;
height: auto;
border-radius: 50%;
margin-bottom: 10px;
}
.staff-member h2, .staff-member p {
margin: 5px 0;
}
/* Responsive Styles */
@media only screen and (max-width: 1024px) {
.staff-member {
width: 48%;
margin: 15px 1%;
}
.staff-member img {
width: 70%;
max-width: 130px;
}
}
@media only screen and (max-width: 768px) {
.staff-member {
width: 100%;
margin: 15px 0;
}
.staff-member img {
width: 60%;
max-width: 120px;
}
</style>
<!-- Navigation -->
<nav class="navbar navbar-expand-lg navbar-dark bg-dark mb-4">
<div class="container">
<!-- Navbar header -->
<a class="navbar-brand" href="#">CSCI 566 Deep Learning and its Applications</a>
<button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<!-- Navbar links -->
<div class="collapse navbar-collapse" id="navbarNav">
<ul class="navbar-nav custom-spacing">
<li class="nav-item"><a class="nav-link" href="index.html">Home</a></li>
<li class="nav-item"><a class="nav-link" href="schedule.html">Course Schedule</a></li>
<li class="nav-item"><a class="nav-link" href="https://docs.google.com/document/d/15o0ZuBrpOlKZdMKj1vlVhp0mmDtdjCgMXEonVB-njbI/edit?usp=sharing">Syllabus (USC login required)</a></li>
</ul>
</div>
</div>
</nav>
<div class="container">
<h2>Schedule</h2>
<p><strong class="university-color">The schedule is subject to change
</strong>: The course website is still <strong>under construction</strong>; please check back frequently.
</p>
<div class="table-responsive">
<table class="table table-bordered">
<!-- Added table-bordered class -->
<thead>
<tr>
<th>Date</th>
<th>Lecture</th>
<th>Homework / Readings</th>
<th>Logistics</th>
</tr>
</thead>
<tbody>
<td colspan="4" style="text-align:center;"><strong>Module 1: ML and DL Foundations</strong></td>
</tr>
<tr>
<td><strong>Week 1</strong><br>Jan 12</td>
<td>
1. Course Introduction<br>
2. Framing ML Problems<br>
3. ML as Function Approximation<br>
4. Linear Models<br>
5. Shape the Course Together
</td>
<td></td>
<td><a href="https://drive.google.com/file/d/1E3DbJHPKuRJPGlEWQgLZueLKgTCnR49A/view?usp=drive_link">S24-block1-intro-linear.pdf</a></td>
</tr>
<tr>
<tr>
<td><strong>Week 2</strong><br>Jan 19</td>
<td>
0. Project idea discussion<br>
1. Classical ML<br>
- Decision Trees and Ensembles<br>
- k-Nearest Neighbors<br>
- Clustering <br>
- Anomaly Detection <br>
2. Cloud computing service tutorial<br>
<!-- <em>Guest Discussion: Life in Data Science Startup (still finalizing)</em>-->
</td>
<td></td>
<td><a href="https://drive.google.com/file/d/1BX6YxF0Ezi4qfSAQ38dF6kTNOtlfnVqC/view?usp=sharing">S24-block2-classical-ML.pdf</a></td>
</tr>
<tr>
<td><strong>Week 3</strong><br>Jan 26</td>
<td>
1. Classical ML (continued)<br>
Neural Network Basics<br>
- Perceptron Revisited <br>
- Gradient Descent <br>
- Forward Propagation <br>
2. Project idea discussion<br>
</td>
<td></td>
<td><a href="https://drive.google.com/file/d/16u4VdChPtLZqvCqKatg_OL65G891lGIB/view?usp=sharing">S24-block3-NeuralNet.pdf</a></td>
</tr>
<tr>
<td><strong>Week 4</strong><br>Feb 2</td>
<td>
1. Neural Network Basics<br>
- Backpropogation Propagation <br>
- Vanishing Gradient <br>
3. Different types of Neural Networks:<br>
- Convolutional Neural Networks<br>
<!-- <em>Guest Discussion: Life in Risk modeling (may change)</em>-->
</td>
<td>Quiz 1</td>
<td>Course Project Teams Formed; Pre-proposal DUE<br> <a href="https://drive.google.com/file/d/10ybuO1DN9vgKoVGjuUU8lHaoZ4Smhwvq/view?usp=drive_link">Google Cloud Platform - 2024.pdf</a><br></td>
</tr>
<tr>
<td><strong>Week 5</strong><br>Feb 9</td>
<td>
1. Different types of Neural Networks:<br>
- Convolutional Neural Networks<br>
2. Deep Learning Software Tutorial (maybe)<br>
</td>
<td>Assignment 1 OUT</td>
<td><a href="https://drive.google.com/file/d/1a9mXW0ENA9lbFfhDDEesvck8RX4lRoQ6/view?usp=drive_link">S24-block4-CNN.pdf</a><br> <a href="https://drive.google.com/file/d/1MrlsvdetWoyXlqPtgphfT-QiME6CKN1a/view?usp=drive_link">S24-block6-GNN.pdf</a><br></td>
</tr>
<tr>
<td><strong>Week 6</strong><br>Feb 16</td>
<td>
Different types of Neural Networks:<br>
1. Recurrent Neural Networks (RNN) & LSTM<br>
2. Graph Neural Networks (GNN)<br>
</td>
<td></td>
<td><a href="https://drive.google.com/file/d/1MrlsvdetWoyXlqPtgphfT-QiME6CKN1a/view?usp=drive_link">S24-block6-GNN.pdf</a><br><a href="https://drive.google.com/file/d/1OxlEdg_POJtafoZeWsgP5yc5ytK9vrxu/view?usp=drive_link">S24-block5-RNN-LSTM.pdf</a></td>
</tr>
<tr>
<td><strong>Week 7</strong><br>Feb 23</td>
<td>MIDTERM EXAM</td>
<td></td>
<td></td>
</tr>
<tr>
<td colspan="4" style="text-align:center;"><strong>Module 2: Deep Learning Applications & Advanced Topics</strong></td>
</tr>
<tr>
<td><strong>Week 8</strong><br>Mar 1</td>
<td>
<strong>Automated ML and Transfer Learning</strong><br>
<em>Guest Discussion: Open-source Development (Dr. Haifeng Jin @ Google)</em>
</td>
<td></td>
<td><a href="https://drive.google.com/file/d/1NmeQamPCm5o4nTCVo_dwO7Zi7vudqflR/view?usp=sharing">S24-block7-Training-AutoML.pdf</a><br>Assignment 1 DUE</td>
</tr>
<tr>
<td><strong>Week 9</strong><br>Mar 8</td>
<td><strong>Training dynamics</strong><br>
<em>Guest discussion: Dr. Souvik Kundu, Research Scientist @ Intel Labs (topic to be decided)</em>
</td>
<td>Assignment 2 OUT</td>
<td></td>
</tr>
<tr>
<td><strong>Week 10</strong><br>Mar 15</td>
<td>NO CLASS; Spring Recess</td>
<td></td>
<td></td>
</tr>
<tr>
<td><strong>Week 11</strong><br>Mar 22</td>
<td>
<strong>Generative AI</strong><br>
1. Generative adversarial networks (GAN)<br>
2. Variational AutoEncoder (VAE)<br>
3. Case Study on Controllable Text Generation<br>
<em>Guest Discussion: Valentino Constantinou, Senior Data Scientist, Team Lead @ Terran Orbital</em>
</td>
<td>Quiz 2</td>
<td><a href="https://drive.google.com/file/d/1-wcwW9sFpwrDJzBWrZHqqfi8zCyzx3vz/view?usp=sharing">S24-block8-GenAI-GAN-VAE.pdf</a><br>Course Project Mid-report DUE</td>
</tr>
<!-- <tr>-->
<!-- <td colspan="4" style="text-align:center;"><strong>Module 3: Advanced Topics in Machine Learning and Deep Learning</strong></td>-->
<!-- </tr>-->
<tr>
<td><strong>Week 12</strong><br>Mar 29</td>
<td>
<strong>Attention, Relation, and Memory Networks</strong><br>
<em>Guest Discussion: Prof. Andrei Irimia, Neural Networks Applications in Healthcare</em>
</td>
<td></td>
<td>Assignment 2 DUE<br><a href="https://drive.google.com/file/d/1Y-CUuApv9KbX0BiCODjVA1-xeqlPYimo/view?usp=drive_link">S24-block9-Attention-relational-memory.pdf</a></td>
</tr>
<tr>
<td><strong>Week 13</strong><br>Apr 5</td>
<td>
<strong>Contrastive Learning and Self-supervised Learning</strong><br>
</td>
<td></td>
<td><a href="https://drive.google.com/file/d/11ETtbEHNuR7MRiuoe7un9xhJ7iXKD959/view?usp=drive_link">S24-block10-SSL-Diffusion-More.pdf</a></td>
</tr>
<tr>
<td><strong>Week 14</strong><br>Apr 12</td>
<td><strong>Reinforcement Learning</strong><br>
<em>Guest discussion: Howie Xu, SVP Engineering AI/ML @ Palo Alto Networks</em></td>
<td></td>
<td><a href="https://drive.google.com/file/d/1V5MPkkxERgd7Mf7fcR-Dea0xqqmh0CAa/view?usp=drive_link">S24-block11-RL.pdf</a></td>
</tr>
<tr>
<td><strong>Week 15</strong><br>Apr 19</td>
<td>Team Project Presentations (zoom; tba)</td>
<td></td>
<td></td>
</tr>
<tr>
<td><strong>Week 16</strong><br>Apr 26</td>
<td>Team Project Presentations (in person)</td>
<td></td>
<td></td>
</tr>
<tr>
<td><strong>Final Report</strong></td>
<td>Final Report Due on University Final Exam Day, Wed May 3</td>
<td>(No in-class Exam)</td>
<td>Final Project Report due
</td>
</tr>
</tbody>
</table>
</div>
</div>
<footer style="background-color: #f9f9f9; padding: 20px; text-align: center; border-top: 2px solid #e5e5e5; margin-top: 50px;">
<p style="font-family: 'Oswald', sans-serif; font-size: 15px; color: #555;">© Copyright 2024 University of Southern California.</p>
<p style="font-family: 'Oswald', sans-serif; font-size: 15px; color: #555;">Written by Yue Zhao and ChatGPT from scratch! <a href="https://github.com/USC-ASAP/CICS566-S24" target="_blank" style="color: #888; text-decoration: none;">Reuse our code</a>.</p>
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