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课程大纲
介绍
概率论、模型选择、决策与信息论
概率分布
用于回归和分类的线性模型
Neural Networks
内核方法
稀疏内核计算机
图形模型
混合物模型和电磁镜
近似推理
抽样方法
连续潜在变量
顺序数据
组合模型
总结和结论
要求
- 对统计学的理解。
- 熟悉多元微积分和基本线性代数。
- 对概率有一定的经验。
观众
- 数据分析师
- 博士生、研究人员和从业人员
21 小时
客户评论 (2)
It felt like we were going through directly relevant information at a good pace (i.e. no filler material)
Maggie Webb - Department of Jobs, Regions, and Precincts
课程 - Introduction to the use of neural networks
Working from first principles in a focused way, and moving to applying case studies within the same day