Information theory, inference and learning algorithms
标签： 编码
上传时间： 2016-04-12
上传用户：baiyouren
To describe Pattern Recognition using Machine Learning Method. It is good for one who want to learn machine learning.
标签： Pattern recognition ML machine learning
上传时间： 2016-04-14
上传用户：shishi
Pattern Recognition and Machine Learning
上传时间： 2016-06-01
上传用户：who123321
Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics
上传时间： 2017-10-27
上传用户：shawnleaves
Q-learning在机器人路径规划中的应用
标签： Q-learning
上传时间： 2018-03-28
上传用户：wangshengmin
强化学习中的Q-Learning在路径规划中的应用
标签： Q-Learning planning path
上传时间： 2018-03-28
上传用户：wangshengmin
Machine learning is a broad and fascinating field. Even today, machine learning technology runs a substantial part of your life, often without you knowing it. Any plausible approach to artifi- cial intelligence must involve learning, at some level, if for no other reason than it’s hard to call a system intelligent if it cannot learn. Machine learning is also fascinating in its own right for the philo- sophical questions it raises about what it means to learn and succeed at tasks.
标签： Learning Machine Course in
上传时间： 2020-06-10
上传用户：shancjb
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
标签： Bishop-Pattern-Recognition-and-Ma chine-Learning
上传时间： 2020-06-10
上传用户：shancjb
This book is a general introduction to machine learning that can serve as a reference book for researchers and a textbook for students. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms.
标签： Foundations Learning Machine 2nd of
上传时间： 2020-06-10
上传用户：shancjb
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
标签： interpretable-machine-learning
上传时间： 2020-06-10
上传用户：shancjb
