图 书 馆开课单位--马克斯普朗克研究所
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81Towards Artificial Systems: What Can We Learn from Human Perception?[走向人工系统：我们可以从人类感知中学到什么？]
Heinrich H. Bülthoff(马克斯普朗克研究所) Recent progress in learning algorithms and sensor hardware has led to rapid advances in artiicial systems. However, their performance continues to fal...
热度：26
Heinrich H. Bülthoff(马克斯普朗克研究所) Recent progress in learning algorithms and sensor hardware has led to rapid advances in artiicial systems. However, their performance continues to fal...
热度：26
82Nature’s Solution to the Problem of Biological Logistics[自然对生物物流问题的解决方案]
Zoltan Maliga(马克斯普朗克研究所) The ability of cells to survive and participate in a community, such as the human body, requires a logistical network that distributes nutrients, cell...
热度：23
Zoltan Maliga(马克斯普朗克研究所) The ability of cells to survive and participate in a community, such as the human body, requires a logistical network that distributes nutrients, cell...
热度：23
83Machine learning[机器教学]
Dominik Janzing(马克斯普朗克研究所) Machine learning has traditionally been focused on prediction. Given observations that have been generated by an unknown stochastic dependency, the go...
热度：79
Dominik Janzing(马克斯普朗克研究所) Machine learning has traditionally been focused on prediction. Given observations that have been generated by an unknown stochastic dependency, the go...
热度：79
84From kernels to causal inference[从内核到因果推理]
Bernhard Schölkopf(马克斯普朗克研究所) Kernel methods in machine learning have expanded from tricks to construct nonlinear algorithms to general tools to assay higher order statistics and p...
热度：77
Bernhard Schölkopf(马克斯普朗克研究所) Kernel methods in machine learning have expanded from tricks to construct nonlinear algorithms to general tools to assay higher order statistics and p...
热度：77
85Cheeger Cuts and p-Spectral Clustering[切痕和P光谱聚类]
Matthias Hein(马克斯普朗克研究所) Spectral clustering has become in recent years one of the most popular clustering algorithm. In this talk I discuss a generalized version of spectral ...
热度：40
Matthias Hein(马克斯普朗克研究所) Spectral clustering has become in recent years one of the most popular clustering algorithm. In this talk I discuss a generalized version of spectral ...
热度：40
86Introduction to bioinformatics[生物信息学概论]
Gunnar Rätsch(马克斯普朗克研究所) I will start by giving a general introduction into Bioinformatics, including basic biology, typical data types (sequences, structures, expression data...
热度：15
Gunnar Rätsch(马克斯普朗克研究所) I will start by giving a general introduction into Bioinformatics, including basic biology, typical data types (sequences, structures, expression data...
热度：15
87Kernel Methods[核方法]
Bernhard Schölkopf(马克斯普朗克研究所) The course will cover some basic ideas of learning theory, elements of the theory of reproducing kernel Hilbert spaces, and some machine learning algo...
热度：61
Bernhard Schölkopf(马克斯普朗克研究所) The course will cover some basic ideas of learning theory, elements of the theory of reproducing kernel Hilbert spaces, and some machine learning algo...
热度：61
88Kernel Methods[核方法]
Bernhard Schölkopf(马克斯普朗克研究所) The course will start with basic ideas of machine learning, followed by some elements of learning theory. It will also introduce positive definite ker...
热度：34
Bernhard Schölkopf(马克斯普朗克研究所) The course will start with basic ideas of machine learning, followed by some elements of learning theory. It will also introduce positive definite ker...
热度：34
89Introduction to kernel methods[内核方法简介]
Bernhard Schölkopf(马克斯普朗克研究所) This lecture given by Mr. Bernhard Schölkop is combined with Mr. Smola and will encompass Part 2, Part 3, Part 4 of the complete lecture. Part 1 , 5, ...
热度：52
Bernhard Schölkopf(马克斯普朗克研究所) This lecture given by Mr. Bernhard Schölkop is combined with Mr. Smola and will encompass Part 2, Part 3, Part 4 of the complete lecture. Part 1 , 5, ...
热度：52
90Learning with Kernels[学习内核]
Bernhard Schölkopf(马克斯普朗克研究所) The course will cover the basics of Support Vector Machines and related kernel methods. # Kernel and Feature Spaces # Large Margin Classification # Ba...
热度：128
Bernhard Schölkopf(马克斯普朗克研究所) The course will cover the basics of Support Vector Machines and related kernel methods. # Kernel and Feature Spaces # Large Margin Classification # Ba...
热度：128