The 6 Best Free Online Artificial Intelligence Courses For 2018
https://www.class-central.com/course/edx-machine-learning-7231
这是 Columbia University
Week 1: maximum likelihood estimation, linear regression, least squares
Week 2: ridge regression, bias-variance, Bayes rule, maximum a posteriori inference
Week 3: Bayesian linear regression, sparsity, subset selection for linear regression
Week 4: nearest neighbor classification, Bayes classifiers, linear classifiers, perceptron
Week 5: logistic regression, Laplace approximation, kernel methods, Gaussian processes
Week 6: maximum margin, support vector machines, trees, random forests, boosting
Week 7: clustering, k-means, EM algorithm, missing data
Week 8: mixtures of Gaussians, matrix factorization
Week 9: non-negative matrix factorization, latent factor models, PCA and variations
Week 10: Markov models, hidden Markov models
Week 11: continuous state-space models, association analysis
Week 12: model selection, next steps
第1周：最大似然估计，线性回归，最小二乘法
第2周：岭回归，偏差 - 方差，贝叶斯规则，最大后验推断
第3周：贝叶斯线性回归，稀疏性，线性回归的子集选择
第4周：最近邻分类，贝叶斯分类器，线性分类器，感知器
第5周：逻辑回归，拉普拉斯近似，核方法，高斯过程
第6周：最大边际，支持向量机，树木，随机森林，提升
第7周：聚类，k均值，EM算法，缺失数据
第8周：高斯混合，矩阵分解
第9周：非负矩阵分解，潜在因子模型，PCA和变化
第10周：马尔可夫模型，隐马尔可夫模型
第11周：连续状态空间模型，关联分析
第12周：模型选择，后续步骤