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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta http-equiv="X-UA-Compatible" content="ie=edge">
<title>Advanced Artificial Intelligence</title>
<link rel="stylesheet" href="semanticui/semantic.min.css">
<srcipt src="semanticui/semantic.min.js"></srcipt>
<link rel="stylesheet" href="katex.min.css">
<script src="katex.min.js"></script>
<link rel="stylesheet" href="common.css">
<style>
body {
font-size: 16px;
}
.main.container {
padding: 2em 0em 7em;
}
.masthead.segment {
padding: 3em 0em;
}
code {
font-size: 0.9em;
background: rgb(229, 230, 231);
border-radius: 3px;
}
@media only screen and (min-width: 1200px) {
.ui.container {
width: 1136px;
}
}
.new,
.new-0106 {
background: rgba(20, 220, 0, 0.25);
}
</style>
</head>
<body>
<!-- Header -->
<div class="ui masthead vertical segment">
<center>
<h1 class="ui header">Advanced Artificial Intelligence (高级人工智能)
<div class="sub header">
SUSTech Autumn Semester
<br>CSE5001(CS421)
<br>Course Material and Useful Links
<br>
<a href="http://www.cs.bham.ac.uk/~xin/" target="_blank">Xin Yao</a> (Chair Professor)
</div>
</h1>
</center>
</div>
<div class="main ui container">
<!-- <div class="ui info message">
<div class="header">Correction</div>
<p>Lecture notes 8, page 41, it should be
<span class="math">l(\beta) = \sum_{i=1}^n\Big(y_i\log p^{\color{#C91808}1}(x_i,\beta) + (1-y_i)\log p^{\color{#C91808}0}(x_i,\beta)\Big)</span>
</p>
</div> -->
<!-- Introduction -->
<h2 class="ui dividing header">Basic Information</h2>
<ul>
<li>
<b>Credits:</b> 3
</li>
<li>
<b>Hours:</b> 64
</li>
<li>
<b>Language:</b> Chinese, English
</li>
<li>
<b>Prerequisites:</b> Artificial Intelligence (人工智能, CS303)
</li>
<li>
<b>Outline:</b> This course introduces recent advances in Artificial Intelligence (AI). Topics covered include intelligent optimisation and learning, as well as case studies in machine learning and pattern recognition. The assessment in the course will consist of homework assignments, a mid-term exam and a final exam.
</li>
<li>
<b>Learning Outcomes:</b> Upon finishing this course, students are expected to have a good understanding of challenging optimisation and learning problems in AI, and different models and algorithms for tackling these problems.
</li>
<li>
<b>Teaching Methods:</b> 2-hour lecture + 2-hour lab on every Friday.
</li>
<li>
<span class="new-0919">
<b>Textbook:</b>
<a href="http://aima.cs.berkeley.edu/" target="_blank">Artificial Intelligence: A Modern Approach</a>
</span>
</li>
</ul>
<!-- Assignment -->
<h2 class="ui dividing header">Assignment 📝</h2>
<ul>
<li>Homework (35%)
<ul>
<li>Every 2 weeks a homework will be assigned. There are 7 in total. Every homework will contribute 5% to the final grade.</li>
<li>Penalty for late submission: 25% for each day late, 0 if the submission is delayed for 3 days or more.</li>
</ul>
</li>
<li>Mid-term test (30%)</li>
<li>Final exam (35%)</li>
</ul>
<!-- Lecture Notes -->
<h2 class="ui dividing header">
Lecture Notes 📜
</h2>
Please find the lecture notes on Sakai.
<table class="ui striped compact celled table">
<thead>
<tr>
<th>Week</th>
<th>Date</th>
<th>Topic</th>
<th>Resources</th>
<th>Homework</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>7 Sept</td>
<td>Introduction & Basic Search I</td>
<td class="new-0907">
TBU
</td>
<td>TBU</td>
</tr>
<tr>
<td>1</td>
<td>7 Sept (afternoon)</td>
<td>Basic Search II</td>
<td class="new-0914">
TBU
</td>
<td>TBU</td>
</tr>
<tr>
<td>2</td>
<td>14 Sept</td>
<td>Heuristic Search</td>
<td class="new-0921">
TBU
</td>
<td>TBU</td>
</tr>
<tr>
<td>3</td>
<td>21 Sept</td>
<td>Meta-Heuristic Search I</td>
<td class="new-0928">
TBU
</td>
<td>TBU</td>
</tr>
<tr>
<td>4</td>
<td>28 Sept</td>
<td>Meta-Heuristic Search II</td>
<td class="new-0930">
TBU
</td>
<td>TBU</td>
</tr>
<tr>
<td>4</td>
<td>30 Sept</td>
<td>Lab work/Tutorial</td>
<td class="new-1012">
TBU
</td>
<td>TBU</td>
</tr>
<tr>
<td>5</td>
<td>12 Oct</td>
<td>Supervised Learning I</td>
<td class="new-1012">
TBU
</td>
<td>TBU</td>
</tr>
<tr>
<td>6</td>
<td>19 Oct</td>
<td>Supervised Learning II</td>
<td class="new-1019">
TBU
</td>
<td>TBU</td>
</tr>
<tr>
<td>7</td>
<td>26 Oct</td>
<td>Ensemble Learning</td>
<td class="new-1026">
TBU
</td>
<td>TBU</td>
</tr>
<tr>
<td>8</td>
<td>2 Nov</td>
<td><b>Mid-term exam</b></td>
<td class="new-1102">
TBU
</td>
<td>TBU</td>
</tr>
<tr>
<td>9</td>
<td>9 Nov</td>
<td>Multi-objective Optimisation and Learning</td>
<td class="new-1109">
TBU
</td>
<td>TBU</td>
</tr>
<tr>
<td>11</td>
<td>23 Nov</td>
<td>
Unsupervised Learning
</td>
<td class="new-1123">
TBU
</td>
<td>
TBU
</td>
</tr>
<tr>
<td>12</td>
<td>30 Nov</td>
<td>Feature Engineering</td>
<td class="new-1130">
TBU
</td>
<td>
TBU
</td>
</tr>
<tr>
<td>13</td>
<td>7 Dec</td>
<td>Markov Decision Process & Reinforcement Learning I</td>
<td class="new-1207">
TBU
</td>
<td>
TBU
</td>
</tr>
<tr>
<td>14</td>
<td>14 Dec</td>
<td>Reinforcement Learning II</td>
<td class="new-1214">
TBU
</td>
<td>
TBU
</td>
</tr>
<tr>
<td>15</td>
<td>21 Dec</td>
<td>Natural Language Processing</td>
<td class="new-1221">
TBU
</td>
<td>
TBU
</td>
</tr>
<tr>
<td>16</td>
<td>28 Dec</td>
<td>
<b>Revision</b>
</td>
<td class="new-1228">
TBU
</td>
<td>
TBU
</td>
</tr>
</tbody>
</table>
<br>
<h2 class="ui dividing header">Lab ⚗️</h2>
<ol>
<li>
TBU
</li>
</ol>
<!-- Suggested Reading -->
<h2 class="ui dividing header">Suggested Reading 📖</h2>
<ul>
<li>
<b>Books</b>
<ul>
<li>Stuart Russell and Peter Norvig, <a href="http://aima.cs.berkeley.edu/" target="_blank">Artificial Intelligence: A Modern Approach (Third edition)</a>, Cambridge University Press, 2009.</li>
</ul>
</li>
<li>
<b>Additional reading materials</b>
<ul>
<li>Relevant papers as handed out at each lecture.</li>
</ul>
</li>
<br>
</ul>
<div class="ui divider"></div>
<p>
<i>Last updated: 2018/<span class="new-0907">06/09</span></i>
</p>
</div>
</body>
</html>