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<table class="aitbl aitbl2">
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<th width="300">主題</th>
<th width="100">天數</th>
<th width="400">內容大綱</th>
</tr>
<tr>
<td width="300">課程介紹 &amp; 人工智慧概觀</td>
<td width="100">1</td>
<td width="400"></td>
</tr>
<tr>
<td width="300">機率、統計與 R 語言</td>
<td width="100">2</td>
<td width="400">1. 敘述性統計 <br>
2. 機率分布 <br>
3. 抽樣與分布 &amp; 假設檢定 <br>
4. 迴歸 &amp; 無母數分析</td>
</tr>
<tr class="tr-fight">
<td width="300">實戰演練－資料分析與資料視覺化</td>
<td width="100">1</td>
<td width="400"></td>
</tr>
<tr>
<td width="300">機器學習基礎與演算法</td>
<td width="100">3</td>
<td width="400">1. 監督式學習
<ul class=margin><li>線性迴歸 (linear regression, LASSO regression)</li>
<li>邏輯式迴歸 (logistic regression)</li>
<li>支持向量機 (support vector machine)</li>
<li>決策樹 (decision tree)</li>
<li>隨機森林 (random forest)</li>
<li>梯度提升機 (gradient boosting machine)</li>
</ul>
2. 非監督式學習
<ul class=margin><li>主成分分析 (principal componet analysis, PCA)</li>
<li>K-means (K-means clustering)</li>
<li>階層式分群 (hierarchical clustering)</li>
<li>DBSCAN</li>
</ul>
</td>
</tr>
<tr>
<td width="300">Python 機器學習程式設計</td>
<td width="100">1</td>
<td width="400"></td>
</tr>
<tr class="tr-fight">
<td width="300">實戰演練－Python 機器學習實作</td>
<td width="100">2</td>
<td width="400"></td>
</tr>
<tr>
<td width="300">深度學習理論：深層神經網路基礎與卷積神經網路 (Deep Neural Network &amp; Convolution Neural Network, DNN &amp; CNN)</td>
<td width="100">3</td>
<td width="400">1. 深度類神經網路
<ul class=margin><li>深度學習理論基礎：backpropagation, stochastic gradient descent, activation functions</li>
<li>神經網路校調：hyperparameter tuning, regularization and optimization</li>
<li>進階優化方式：batch normalization, Swish activation, etc.</li>
</ul>
2. 卷積神經網路
<ul class=margin><li>卷積過濾器與影像資料處理</li>
<li>Data augmentation</li>
<li>轉移學習</li>
</ul>
</td>
</tr>
<tr>
<td width="300">TensorFlow 程式設計</td>
<td width="100">1</td>
<td width="400">1. TensorFlow basics <br>
2. How to build up a model? <br>
3. Model training<br>
4. Model inference</td>
</tr>
<tr class="tr-exam">
<td width="300">期中考</td>
<td width="100">1</td>
<td width="400"></td>
</tr>
<tr class="tr-fight">
<td width="300">實戰演練－DNN &amp; CNN</td>
<td width="100">2</td>
<td width="400"></td>
</tr>
<tr>
<td width="300">深度學習理論：遞迴神經網路 (Recurrent Neural Network, RNN)</td>
<td width="100">1</td>
<td width="400">序列資料與遞迴神經網路
<ul><li>Gated recurrent unit (GRU) and Long short-term memory (LSTM)</li>
<li>時序資料處理與預測性維護</li>
<li>自然語言處理與音頻資料處理</li>
</ul>
</td>
</tr>
<tr class="tr-fight">
<td width="300">實戰演練－RNN</td>
<td width="100">1</td>
<td width="400"></td>
</tr>
<tr>
<td width="300">深度學習理論：生成式對抗網路 (Generative Adversarial Network, GAN)</td>
<td width="100">1</td>
<td width="400">生成式對抗網路 (GANs)
<ul><li>何謂生成式對抗網路</li>
<li>進階生成式對抗網路 (WGAN, EBGAN, infoGAN, etc.)</li>
<li>Training tips of GANs</li>
</ul>
</td>
</tr>
<tr class="tr-fight">
<td width="300">實戰演練－GAN</td>
<td width="100">2</td>
<td width="400"></td>
</tr>
<tr>
<td width="300">深度學習理論：強化學習 (Reinforcement Learning, RL)</td>
<td width="100">2</td>
<td width="400">強化學習基礎
<ul><li>Markov decision process</li>
<li>Q-learning &amp; Deep-Q Network</li>
<li>Policy gradient</li>
<li>Asynchronous Advantage Actor-Critic (A3C), Deep Deterministic Policy Gradient (DDPG)</li>
</ul>
</td>
</tr>
<tr class=tr-fight>
<td width="300">實戰演練－Reinforcement Learning</td>
<td width="100">2</td>
<td width="400"></td>
</tr>
<tr>
<td width="300">深度學習於電腦視覺之應用 (Computer Vision)</td>
<td width="100">1</td>
<td width="400">1. 經典神經網路比較：VGG, ResNet &amp; DenseNet <br>
2. 深度學習於電腦視覺應用
<ul class=margin><li>影像分類 (image classification)</li>
<li>物體偵測 (object detection)</li>
<li>影像分割 (image segmentation)</li>
<li>影像描述 (image captioning)</li>
</ul>
</td>
</tr>
<tr class="tr-fight">
<td width="300">實戰演練－Computer Vision</td>
<td width="100">1</td>
<td width="400"></td>
</tr>
<tr>
<td width="300">深度學習於自然語言處理之應用 (Natural Language Processing, NLP)</td>
<td width="100">1</td>
<td width="400">深度學習於
<ul><li>聊天機器人 (chatbot)</li>
<li>情緒分析 (sentiment analysis)</li>
<li>文字摘要 (text summarization)</li>
</ul>
</td>
</tr>
<tr class="tr-fight">
<td width="300">實戰演練－Natural Language Processing</td>
<td width="100">1</td>
<td width="400"></td>
</tr>
<tr class="tr-subject">
<td width="300">期末專題實作</td>
<td width="100">26</td>
<td width="400"></td>
</tr>
<tr class="tr-exam">
<td width="300">期末考</td>
<td width="100">1</td>
<td width="400"></td>
</tr>
<tr class="tr-exam">
<td width="300">期末成果發表會</td>
<td width="100">1</td>
<td width="400"></td>
</tr>
<tr class="tr-totalhours">
<td width="300">總天數</td>
<td width="100">58</td>
<td width="400"></td>
</tr>
</tbody>
</table>