The 6 Best Free Online Artificial Intelligence Courses For 2018

這是 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：最大似然估計，線性回歸，最小二乘法

第2周：嶺回歸，偏差 - 方差，貝葉斯規則，最大後驗推斷

第3周：貝葉斯線性回歸，稀疏性，線性回歸的子集選擇

第4周：最近鄰分類，貝葉斯分類器，線性分類器，感覺器

第5周：邏輯回歸，拉普拉斯近似，核方法，高斯過程

第6周：最大邊際，支援向量機，樹木，随機森林，提升

第7周：聚類，k均值，EM算法，缺失資料

第8周：高斯混合，矩陣分解

第9周：非負矩陣分解，潛在因子模型，PCA和變化

第10周：馬爾可夫模型，隐馬爾可夫模型

第11周：連續狀态空間模型，關聯分析

第12周：模型選擇，後續步驟