Basis(基礎)：
- SSE(Sum of Squared Error, 平方誤差和)
- SAE(Sum of Absolute Error, 絕對誤差和)
- SRE(Sum of Relative Error, 相對誤差和)
- MSE(Mean Squared Error, 均方誤差)
- RMSE(Root Mean Squared Error, 均方根誤差)
- RRSE(Root Relative Squared Error, 相對平方根誤差)
- MAE(Mean Absolute Error, 平均絕對誤差)
- RAE(Root Absolute Error, 平均絕對誤差平方根)
- MRSE(Mean Relative Square Error, 相對平均誤差)
- RRSE(Root Relative Squared Error, 相對平方根誤差)
- Expectation(期望)&Variance(方差)
- Standard Deviation(標準差，也稱Root Mean Squared Error, 均方根誤差)
- CP(Conditional Probability, 條件概率)
- JP(Joint Probability, 聯合概率)
- MP(Marginal Probability, 邊緣概率)
- Bayesian Formula(貝葉斯公式)
- CC(Correlation Coefficient, 相關系數)
- Quantile (分位數)
- Covariance(協方差矩陣)
- GD(Gradient Descent, 梯度下降)
- SGD(Stochastic Gradient Descent, 隨機梯度下降)
- LMS(Least Mean Squared, 最小均方)
- LSM(Least Square Methods, 最小二乘法)
- NE(Normal Equation, 正規方程)
- MLE(Maximum Likelihood Estimation, 極大似然估計)
- QP(Quadratic Programming, 二次規劃)
- L1 /L2 Regularization(L1/L2正則, 以及更多的, 現在比較火的L2.5正則等)
- Eigenvalue(特征值)
- Eigenvector(特征向量)
Common Distribution(常見分布)：
Discrete Distribution(離散型分布)：
- Bernoulli Distribution/Binomial Distribution(貝努利分布/二項分布)
- Negative Binomial Distribution(負二項分布)
- Multinomial Distribution(多項分布)
- Geometric Distribution(幾何分布)
- Hypergeometric Distribution(超幾何分布)
- Poisson Distribution (泊松分布)
Continuous Distribution (連續型分布)：
- Uniform Distribution(均勻分布)
- Normal Distribution/Gaussian Distribution(正態分布/高斯分布)
- Exponential Distribution(指數分布)
- Lognormal Distribution(對數正態分布)
- Gamma Distribution(Gamma分布)
- Beta Distribution(Beta分布)
- Dirichlet Distribution(狄利克雷分布)
- Rayleigh Distribution(瑞利分布)
- Cauchy Distribution(柯西分布)
- Weibull Distribution (韋伯分布)
Three Sampling Distribution(三大抽樣分布)：
- Chi-square Distribution(卡方分布)
- t-distribution(t-分布)
- F-distribution(F-分布)
Data Pre-processing(數據預處理)：
- Missing Value Imputation(缺失值填充)
- Discretization(離散化)
- Mapping(映射)
- Normalization(歸一化/標準化)
Sampling(采樣)：
- Simple Random Sampling(簡單隨機采樣)
- Offline Sampling(離線等可能K采樣)
- Online Sampling(在線等可能K采樣)
- Ratio-based Sampling(等比例隨機采樣)
- Acceptance-rejection Sampling(接受-拒絕采樣)
- Importance Sampling(重要性采樣)
- MCMC(Markov Chain MonteCarlo 馬爾科夫蒙特卡羅采樣算法：Metropolis-Hasting& Gibbs)
Clustering(聚類)：
- K-MeansK-Mediods
- 二分K-Means
- FK-Means
- Canopy
- Spectral-KMeans(譜聚類)
- GMM-EM(混合高斯模型-期望最大化算法解決)
- K-Pototypes
- CLARANS(基于劃分)
- BIRCH(基于層次)
- CURE(基于層次)
- STING(基于網格)
- CLIQUE(基于密度和基于網格)
- 2014年Science上的密度聚類算法等
Clustering Effectiveness Evaluation(聚類效果評估)：
- Purity(純度)
- RI(Rand Index, 芮氏指標)
- ARI(Adjusted Rand Index, 調整的芮氏指標)
- NMI(Normalized Mutual Information, 規范化互信息)
- F-meaure(F測量)
Classification&Regression(分類&回歸)：
- LR(Linear Regression, 線性回歸)
- LR(Logistic Regression, 邏輯回歸)
- SR(Softmax Regression, 多分類邏輯回歸)
- GLM(Generalized Linear Model, 廣義線性模型)
- RR(Ridge Regression, 嶺回歸/L2正則最小二乘回歸)，LASSO(Least Absolute Shrinkage and Selectionator Operator , L1正則最小二乘回歸)
- DT(Decision Tree決策樹)
- RF(Random Forest, 隨機森林)
- GBDT(Gradient Boosting Decision Tree, 梯度下降決策樹)
- CART(Classification And Regression Tree 分類回歸樹)
- KNN(K-Nearest Neighbor, K近鄰)
- SVM(Support Vector Machine, 支持向量機, 包括SVC(分類)&SVR(回歸))
- CBA(Classification based on Association Rule, 基于關聯規則的分類)
- KF(Kernel Function, 核函數)?
- Polynomial Kernel Function(多項式核函數)
- Guassian Kernel Function(高斯核函數)
- Radial Basis Function(RBF徑向基函數)
- String Kernel Function 字符串核函數
- NB(Naive Bayesian,樸素貝葉斯)
- BN(Bayesian Network/Bayesian Belief Network/Belief Network 貝葉斯網絡/貝葉斯信度網絡/信念網絡)
- LDA(Linear Discriminant Analysis/Fisher Linear Discriminant 線性判別分析/Fisher線性判別)
- EL(Ensemble Learning, 集成學習)?
- Boosting
- Bagging
- Stacking
- AdaBoost(Adaptive Boosting 自適應增強)
- MEM(Maximum Entropy Model, 最大熵模型)
Classification EffectivenessEvaluation(分類效果評估)：
- Confusion Matrix(混淆矩陣)
- Precision(精確度)
- Recall(召回率)
- Accuracy(準確率)
- F-score(F得分)
- ROC Curve(ROC曲線)
- AUC(AUC面積)
- Lift Curve(Lift曲線)
- KS Curve(KS曲線)
PGM(Probabilistic Graphical Models, 概率圖模型)：
- BN(BayesianNetwork/Bayesian Belief Network/ Belief Network , 貝葉斯網絡/貝葉斯信度網絡/信念網絡)
- MC(Markov Chain, 馬爾科夫鏈)
- MEM(Maximum Entropy Model, 最大熵模型)
- HMM(Hidden Markov Model, 馬爾科夫模型)
- MEMM(Maximum Entropy Markov Model, 最大熵馬爾科夫模型)
- CRF(Conditional Random Field,條件隨機場)
- MRF(Markov Random Field, 馬爾科夫隨機場)
- Viterbi(維特比算法)
NN(Neural Network, 神經網絡)
- ANN(Artificial Neural Network, 人工神經網絡)
- SNN(Static Neural Network, 靜態神經網絡)
- BP(Error Back Propagation, 誤差反向傳播)
- HN(Hopfield Network)
- DNN(Dynamic Neural Network, 動態神經網絡)
- RNN(Recurrent Neural Network, 循環神經網絡)
- SRN(Simple Recurrent Network, 簡單的循環神經網絡)
- ESN(Echo State Network, 回聲狀態網絡)
- LSTM(Long Short Term Memory, 長短記憶神經網絡)
- CW-RNN(Clockwork-Recurrent Neural Network, 時鐘驅動循環神經網絡, 2014ICML）等.
Deep Learning(深度學習)：
- Auto-encoder(自動編碼器)
- SAE(Stacked Auto-encoders堆疊自動編碼器)?
- Sparse Auto-encoders(稀疏自動編碼器)
- Denoising Auto-encoders(去噪自動編碼器)
- Contractive Auto-encoders(收縮自動編碼器)
- RBM(Restricted Boltzmann Machine, 受限玻爾茲曼機)
- DBN(Deep Belief Network, 深度信念網絡)
- CNN(Convolutional Neural Network, 卷積神經網絡)
- Word2Vec(詞向量學習模型)
Dimensionality Reduction(降維)：
- LDA(Linear Discriminant Analysis/Fisher Linear Discriminant, 線性判別分析/Fish線性判別)
- PCA(Principal Component Analysis, 主成分分析)
- ICA(Independent Component Analysis, 獨立成分分析)
- SVD(Singular Value Decomposition 奇異值分解)
- FA(Factor Analysis 因子分析法)
Text Mining(文本挖掘)：
- VSM(Vector Space Model, 向量空間模型)
- Word2Vec(詞向量學習模型)
- TF(Term Frequency, 詞頻)
- TF-IDF(TermFrequency-Inverse Document Frequency, 詞頻-逆向文檔頻率)
- MI(Mutual Information, 互信息)
- ECE(Expected Cross Entropy, 期望交叉熵)
- QEMI(二次信息熵)
- IG(Information Gain, 信息增益)
- IGR(Information Gain Ratio, 信息增益率)
- Gini(基尼系數)
- x2 Statistic(x2統計量)
- TEW(Text Evidence Weight, 文本證據權)
- OR(Odds Ratio, 優勢率)
- N-Gram Model
- LSA(Latent Semantic Analysis, 潛在語義分析)
- PLSA(Probabilistic Latent Semantic Analysis, 基于概率的潛在語義分析)
- LDA(Latent Dirichlet Allocation, 潛在狄利克雷模型)
- SLM(Statistical Language Model, 統計語言模型)
- NPLM(Neural Probabilistic Language Model, 神經概率語言模型)
- CBOW(Continuous Bag of Words Model, 連續詞袋模型)
- Skip-gram(Skip-gram Model)
Association Mining(關聯挖掘)：
- Apriori算法
- FP-growth(Frequency Pattern Tree Growth, 頻繁模式樹生長算法)
- MSApriori(Multi Support-based Apriori, 基于多支持度的Apriori算法)
- GSpan(Graph-based Substructure Pattern Mining, 頻繁子圖挖掘)
Sequential Patterns Analysis(序列模式分析)
- AprioriAll
- Spade
- GSP(Generalized Sequential Patterns, 廣義序列模式)
- PrefixSpan
Forecast(預測)
- LR(Linear Regression, 線性回歸)
- SVR(Support Vector Regression, 支持向量機回歸)
- ARIMA(Autoregressive Integrated Moving Average Model, 自回歸積分滑動平均模型)
- GM(Gray Model, 灰色模型)
- BPNN(BP Neural Network, 反向傳播神經網絡)
- SRN(Simple Recurrent Network, 簡單循環神經網絡)
- LSTM(Long Short Term Memory, 長短記憶神經網絡)
- CW-RNN(Clockwork Recurrent Neural Network, 時鐘驅動循環神經網絡)
- ……
Linked Analysis(鏈接分析)
- HITS(Hyperlink-Induced Topic Search, 基于超鏈接的主題檢索算法)
- PageRank(網頁排名)
Recommendation Engine(推薦引擎)：
- SVD
- Slope One
- DBR(Demographic-based Recommendation, 基于人口統計學的推薦)
- CBR(Context-based Recommendation, 基于內容的推薦)
- CF(Collaborative Filtering, 協同過濾)
- UCF(User-based Collaborative Filtering Recommendation, 基于用戶的協同過濾推薦)
- ICF(Item-based Collaborative Filtering Recommendation, 基于項目的協同過濾推薦)
Similarity Measure&Distance Measure(相似性與距離度量)：
- EuclideanDistance(歐式距離)
- Chebyshev Distance(切比雪夫距離)
- Minkowski Distance(閔可夫斯基距離)
- Standardized EuclideanDistance(標準化歐氏距離)
- Mahalanobis Distance(馬氏距離)
- Cos(Cosine, 余弦)
- Hamming Distance/Edit Distance(漢明距離/編輯距離)
- Jaccard Distance(杰卡德距離)
- Correlation Coefficient Distance(相關系數距離)
- Information Entropy(信息熵)
- KL(Kullback-Leibler Divergence, KL散度/Relative Entropy, 相對熵)
Optimization(最優化)：
Non-constrained Optimization(無約束優化)：
- Cyclic Variable Methods(變量輪換法)
- Variable Simplex Methods(可變單純形法)
- Newton Methods(牛頓法)
- Quasi-Newton Methods(擬牛頓法)
- Conjugate Gradient Methods(共軛梯度法)。
Constrained Optimization(有約束優化)：
- Approximation Programming Methods(近似規劃法)
- Penalty Function Methods(罰函數法)
- Multiplier Methods(乘子法)。
- Heuristic Algorithm(啟發式算法)
- SA(Simulated Annealing, 模擬退火算法)
- GA(Genetic Algorithm, 遺傳算法)
- ACO(Ant Colony Optimization, 蟻群算法)
Feature Selection(特征選擇)：
- Mutual Information(互信息)
- Document Frequence(文檔頻率)
- Information Gain(信息增益)
- Chi-squared Test(卡方檢驗)
- Gini(基尼系數)
Outlier Detection(異常點檢測)：
- Statistic-based(基于統計)
- Density-based(基于密度)
- Clustering-based(基于聚類)。
Learning to Rank(基于學習的排序)：
- Pointwise?
- McRank
- Pairwise?
- RankingSVM
- RankNet
- Frank
- RankBoost；
- Listwise?
- AdaRank
- SoftRank
- LamdaMART
Tool(工具)：
- MPI
- Hadoop生態圈
- Spark
- IGraph
- BSP
- Weka
- Mahout
- Scikit-learn
- PyBrain
- Theano?
