高难度机器学习代考高分
主要考察了perceptron, validation 和 Expectation-maximization method.
机器学习代写代考 machine learning
机器学习作为CS必修课程之一, 一般包括supervised learning监督学习, unsupervised learning 无监督学习和reinforcement learning(RL)增强学习3个大方向.
supervised learning通常需要学习linear regression, decision tree, SVM(support vector machine), logistic regression, naive bayes, random forest,
Neural Networks神经网络等内容.
深度学习deep learning是现在非常热门的方向, 需要学习CNN (convolution neural network), RNN (recurrent neural network)等网络架构. 知识点包括dropout, backpropagation, pooling, convolutional layer等. 深度学习现在已经广泛应用到计算机视觉 (computer vision) 和 NLP (自然语言处理).
unsupervised learning通常要学习principal component analysis (PCA), factor analysis, clustering algorithm such as K-means, EM (Expectation–maximization algorithm), GMM (gaussian mixture model)等.
reinforcement learning一般会学习Q-learning和Deep Q-learning.
机器学习代考高分
Machine learning代考接近满分, 课本使用的是经典教材 The Elements of Statistical Learning. 考了Linear Regression, PCA, EM算法, Neural network 神经网络, perceptron, SVM, decision tree, random forest, boost等内容.
机器学习代考高分
这次Machine learning考试范围包括classification, regression, unsupervised learning, deep learning, feature engineering等. 2小时做接近40道题包括问答题, 题量非常大, 最终取得85-90的好成绩.