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課程簡介
介紹
概率論、模型選擇、決策與資訊論
概率分佈
用於回歸和分類的線性模型
Neural Networks
內核方法
稀疏內核計算機
圖形模型
混合物模型和電磁鏡
近似推理
抽樣方法
連續潛在變數
順序數據
組合模型
總結和結論
最低要求
- 對統計學的理解。
- 熟悉多元微積分和基本線性代數。
- 對概率有一定的經驗。
觀眾
- 數據分析師
- 博士生、研究人員和從業人員
21 時間:
客戶評論 (3)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
Very flexible