9. What is Bayes’s Theorem in Machine Learning? 10. What is PCA in Machine Learning? Basic Machine Learning Interview Questions 1. What is Bias and Variance in Machine Learning? Bias is a statistical measure that indicates the difference between actual and predicted values—the more the differ...
Basic Machine Learning Interview Questions Basic questions are related to terminologies, algorithms, and methodologies. Interviewers ask these questions to assess the technical knowledge of the candidate. 1. What is Semi-supervised Machine Learning? Semi-supervised learning is the blend of supervised and...
2.1 supervised learning(used most) -从 "正确答案 "中学习 data comes with input x and output y regression:学习输入、输出或 x 到 y 的映射,以预测数字 classification: 预测类别(可能输出的有限小集合,既可以是数字,也可以是非数字) 2.2 unsupervised learning -从未标明的数据中发现有趣的东西 data only...
Decision trees: Decision trees use supervised learning and basic if-then progressions to make predictions. Depending on the complexity of the project, decision trees can be ideal as resource-light algorithms that produce straightforward results. For example, if a college wanted to determine which stud...
Machine Learning Basic Knowledge 常用的数据挖掘&机器学习知识(点) Basis(基础): MSE(MeanSquare Error 均方误差),LMS(Least MeanSquare 最小均方),LSM(Least Square Methods 最小二乘法),MLE(Maximum LikelihoodEstimation最大似然估计),QP(QuadraticProgramming 二次规划), CP(ConditionalProbability条件概率),JP(...
This Machine Learning tutorial is for anyone who wants to learn about machine learning. No prior knowledge of machine learning is required. Read the tutorial to learn more about machine learning.
Section 3 answers the following three basic questions: What data do we need to capture the optimal behaviour of the couple (EA and PM)? How do we generate the datasets for the ML algorithm? What ML model can be used to design an appropriate controller (we will name it ML controller)?
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Decision trees: Decision trees use supervised learning and basic if-then progressions to make predictions. Depending on the complexity of the project, decision trees can be ideal as resource-light algorithms that produce straightforward results. For example, if a college wanted to determine which stud...
Information extraction: Ask questions over databases across the web. Social networks: Data on relationships and preferences. Machine learning to extract value from data. Debugging: Use in computer science problems like debugging. Labor intensive process. Could suggest where the bug could be. ...