國外小組科研—人工智能與深度學習專題： 告別“機器假學習”，下一代AI熱潮：因果推斷與強人工智能實現(xiàn)路徑研究【大學組】

開始日期：

2023年7月8日

專業(yè)方向：

計算機與人工智能

導師：

Nicholas （劍橋大學 University of Cambridge 終身教授）

課程周期：

7周在線小組科研學習+5周不限時論文指導學習

語言：

英文

建議學生年級：

大學生

項目產(chǎn)出：

7周在線小組科研學習+5周不限時論文指導學習 共125課時 項目報告 優(yōu)秀學員獲主導師Reference Letter EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等級別索引國際會議全文投遞與發(fā)表指導（可用于申請） 結業(yè)證書 成績單

項目介紹：

項目內(nèi)容包括機器學習理論、應用與技術，線性分類器，卷積神經(jīng)網(wǎng)絡，回歸神經(jīng)網(wǎng)絡等。學生將通過項目熟悉機器學習、神經(jīng)網(wǎng)絡、深度神經(jīng)網(wǎng)絡等理論知識與機器學習語音與圖像處理案例，在項目結束時，提交項目報告，進行成果展示。 This course aims to provide an introduction to the fundamentals of deep learning. It will cover the most common forms of model architectures and primarily the algorithms used to train them. Attention will also be payed to how deep learning manifests in both distributed and constrained compute platforms (e.g., computing clusters, wearables and phones). Theory and principles will be presented, but this will go hand-in-hand with a focus on practical experience such as using existing frameworks and implementing (simplified versions) of core algorithms. Students will be taught the basics of neural networks, convolutional networks, recurrent networks; and introduced to concepts such as: dropout, batch normalization, and types of hyper-parameter optimization. Applications in the area of audio and image processing will be discussed.