杨立昆（Yann LeCun） 杰弗里·欣顿（Geoffrey Hinton） 本吉奥（ Bengio ） 共同获得了2018年计算机科学的最高奖项 ——ACM图灵奖。 机器学习界的执牛耳者 Andrew Ng 中文名吴恩达，斯坦福大学副教 授，前“百度大脑”的负责人与百 度首席科学家。 6 李航, 现任字节跳动科技有限公司人 工智能实验室总监，北京大学、南京 大学客座教授，IEEE 会士，ACM 杰 ，都有相近的高准确度 。于是诞生了机器学习 界的名言： 成功的机器学习应 用不是拥有最好的 算法，而是拥有最 多的数据！ 数据决定一切 数据大小 准 确 率 77 参考文献 [1] Andrew Ng. Machine Learning[EB/OL]. StanfordUniversity,2014.https://www.coursera.org/course/ml [2] 李航. 统计学习方法[M] Friedman J. The Elements of Statistical Learning[M]. New York: Springer,2001. [5] CHRISTOPHER M. BISHOP. Pattern Recognition and Machine Learning[M]. New York: Springer,2006. [6] Stephen Boyd, Lieven

[1] HOSMER D W, LEMESHOW S, STURDIVANT R X. Applied logistic regression[M]. New Jersey: Wiley New York.2000. [2] Andrew Ng. Machine Learning[EB/OL]. Stanford University,2014. https://www.coursera.org/course/ml Friedman J. The Elements of Statistical Learning[M]. New York: Springer,2001. [5] CHRISTOPHER M. BISHOP. Pattern Recognition and Machine Learning[M]. New York: Springer,2006. [6] Stephen Boyd, Lieven

TOM M MICHELLE. Machine Learning[M]. New York: McGraw-Hill Companies,Inc,1997. [2] Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning[M]. New York: Springer,2001. [3] CHRISTOPHER CHRISTOPHER M. BISHOP. Pattern Recognition and Machine Learning[M]. New York: Springer,2006. [4] Zhang H., The optimality of naïve Bayes[C]//Proceedings of the 17th International Florida Artificial Intelligence Intelligence Research Society Conference (FLAIRS), Miami, FL, 562-567, 2004. [5] Ng A. Y. and M. I. Jordan, On discriminative vs. generative classifiers: A comparison of logistic regression and naïve Bayes[C]//

≤ L(µ(t−1), X, Z(t−1)) because the new Z(t) = arg minZ L(µ(t−1), X, Z) When we update µ from µ(t−1) to µ(t) L(µ(t), X, Z(t)) ≤ L(µ(t−1), X, Z(t)) because the new µ(t) = arg minµ L(µ, X, Z(t)) Feng clusters MST-based method: Build a minimum spanning tree from the dissim- ilarity graph, and then make new clusters by breaking the link corre- sponding to the largest dissimilarity Feng Li (SDU) K-Means December containing all the data G Measure the average dissimilarity of i ∈ G to all the other i′ ∈ G dG i = 1 nG � i′∈G di,i′ Remove the most dissimilar data i∗ and put it in its own cluster H i∗ = arg max i∈G

Andrew Ng. Machine Learning[EB/OL]. StanfordUniversity,2014. https://www.coursera.org/course/ml [2] Peter Harrington.机器学习实战[M]. 北京:人民邮电出版社,2013. [3] TOM M MICHELLE. Machine Learning[M]. New York: McGraw-Hill Friedman J. The Elements of Statistical Learning[M]. New York: Springer,2001. [5] CHRISTOPHER M. BISHOP. Pattern Recognition and Machine Learning[M]. New York: Springer,2006. [6] Kohavi R.,Scaling up the

当回溯到根节点时，算法结束，此时保存 的最近邻节点就是最终的最近邻。 遍历完（4,5）的左右叶子节点，发现与当 前最优距离相等，不更新最近邻。 所以（4,4）的最近邻为（4,5）。 25 参考文献 [1] Andrew Ng. Machine Learning[EB/OL]. StanfordUniversity,2014.https://www.coursera.org/course/ml [2] 李航. 统计学习方法[M] Friedman J. The Elements of Statistical Learning[M]. New York: Springer,2001. [6] CHRISTOPHER M. BISHOP. Pattern Recognition and Machine Learning[M]. New York: Springer,2006. [7] Stephen Boyd, Lieven

CORTES C, VAPNIK V. Support-vector networks[J]. Machine learning, 1995, 20(3): 273–297. [2] Andrew Ng. Machine Learning[EB/OL]. StanfordUniversity,2014.https://www.coursera.org/course/ml [3] 李航. 统计学习方法[M] Friedman J. The Elements of Statistical Learning[M]. New York: Springer,2001. [5] CHRISTOPHER M. BISHOP. Pattern Recognition and Machine Learning[M]. New York: Springer,2006. [6] Stephen Boyd, Lieven

2016. [3] Andrew Ng. Machine Learning[EB/OL]. StanfordUniversity,2014. https://www.coursera.org/course/ml [4] CHRISTOPHER M. BISHOP. Pattern Recognition and Machine Learning[M]. New York: Springer,2006

 创建与另一个张量具有相同大小的张量，请使用 torch.*_like  如torch.rand_like()  创建与其他张量具有相似类型但大小不同的张量，请使 用tensor.new_*创建操作。 1.Tensors张量的概念 6  查看张量的属性  查看Tensor类型  tensor1 = torch.randn(2,3) #形状为(2,3)一组从标准正态分布 Adam(model.parameters(), lr=learning_rate) 39 参考文献 1. IAN GOODFELLOW等，《深度学习》，人民邮电出版社，2017 2. Andrew Ng，http://www.deeplearning.ai 3. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer-Verlag

主成分各个特征维度的含义具有一定的模糊性,不如原始样本特征的解释性强 2.方差小的非主成分也可能含有对样本差异的重要信息,因降维丢弃可能对后续数 据处理有影响 50 参考文献 [1] Andrew Ng. Machine Learning[EB/OL]. StanfordUniversity,2014.https://www.coursera.org/course/ml [2] Hinton 清华大学出版社,2019. [5] Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning[M]. New York: Springer,2001. [6] Peter Harrington.机器学习实战[M]. 北京:人民邮电出版社,2013. 51 谢 谢！