The courses included in this Track are: Unsupervised Learning in Python, Linear Classifiers in Python, Supervised Learning with scikit-learn, and Introduction to Deep Learning with PyTorch. What is the average time commitment for this Track?
教材:https://github.com/harvard-ml-courses/cs181-textbook/blob/master/Textbook.pdf 从课程大纲中可以看出,这门课非常全面严谨地介绍了机器学习、概率推理等基础知识。 内容涵盖了监督学习、集成方法与提升方法、神经网络、支持向量机、核方...
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哈佛的CS 1810课程,此前在网上就曾引起热议。 教材:https://github.com/harvard-ml-courses/cs181-textbook/blob/master/Textbook.pdf 从课程大纲中可以看出,这门课非常全面严谨地介绍了机器学习、概率推理等基础知识。 内容涵盖了监督学习、集成方法与提升方法、神经网络、支持向量机、核方法、聚类与无监督学习、最...
而且这门课因为是面向高年级本科生,所以需要学生能用Python写比较复杂的程序,还需要具备概率论、微积分、线代的基础知识。 正如此前提到的助理教授所说,这份课程大纲中保留了很多深度学习前期的基础概念,这在当今竟然已经不多见了。 MIT 另一传统牛校MIT 2024年的机器学习讲义,是下面这样的。 教材:https://introml...
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OUR TAKE:Udacity’s nanodegree program takes roughly 3 months to complete (at 5-10 hours per week). Students should bring prior experience with Python, machine learning, and statistics for the greatest chance of success with this module. ...
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