图 书 馆
机器学习Machine Learning |
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| 课程网址: | http://videolectures.net/deeplearning2016_precup_machine_learning... |
| 主讲教师: | Doina Precup |
| 开课单位: | 麦吉尔大学 |
| 开课时间: | 2016-08-23 |
| 课程语种: | 英语 |
| 中文简介: |  我们提供了机器学习的一般介绍，旨在使所有参与者在定义和基本背景方面都处于同一页面上。在简要概述了不同的机器学习问题之后，我们讨论了线性回归，其目标函数和闭式解。我们讨论了偏差方差的权衡和过度拟合的问题（以及正确使用交叉验证来客观地衡量绩效）。在数据生成过程的特定假设下，我们讨论了平方和误差作为最大似然性的概率观点，并从贝叶斯的角度介绍了先验的L2和L1正则化方法。我们简要讨论了贝叶斯学习方法。最后，我们通过一阶和二阶方法介绍了逻辑回归，交叉熵优化准则及其解决方案。 |
| 课程简介: | We provide a general introduction to machine learning, aimed to put all participants on the same page in terms of definitions and basic background. After a brief overview of different machine learning problems, we discuss linear regression, its objective function and closed-form solution. We discuss the bias-variance trade-off and the issue of overfitting (and the proper use of cross-validation to measure performance objectively). We discuss the probabilistic view of the sum-squared error as maximizing likelihood under specific assumptions on the data generation process, and present L2 and L1 regularization methods as priors from a Bayesian perspective. We briefly discuss Bayesian methodology for learning. Finally, we present logistic regression, the cross-entropy optimization criterion and its solution through first- and second-order methods. |
| 关 键 词: | 机器学习; 线性回归; 贝叶斯; 逻辑回归 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-03-25：zhouxj |
| 最后编审: | 2020-05-25：cxin |
| 阅读次数: | 59 |