此參考說明所註明的目錄。請向該機構確認當前課程供給與適用於你入學的條件。
課程說明
計算統計 分類：邏輯斯迴歸、線性與二次判別分析；重抽樣方法：交叉驗證與自助法；線性模型選擇與正規化：子集選擇、收縮方法、降維方法、高維度下之考量；非線性迴歸：多項式迴歸、迴歸樣條、平滑樣條、局部迴歸與廣義加性模型；樹狀方法：決策樹、bagging、隨機森林與提升法；支援向量機：最大間隔分類器、支援向量分類器、支援向量機（SVM）、多類別 SVM；非監督學習：主成分分析、分群方法。每週講授 3 小時；實驗 1 小時。限制：僅開放 Data Sciences and Analytics 主修。先修：STAT 3P82 或 STAT 3P86 或 STAT (DASA) 3P87 其一，或授課教師許可。
先修條件
- 先修：STAT 3P82 或 STAT 3P86 或 STAT (DASA) 3P87 其一，或授課教師許可。
條件與方式
- 限制：僅開放 Data Sciences and Analytics 主修。
- 先修：STAT 3P82 或 STAT 3P86 或 STAT (DASA) 3P87 其一，或授課教師許可。
原文參考文本
Computational Statistics Classification: logistic regression, linear and quadratic discriminant analysis; Resampling methods: cross-validation and bootstrap; Linear model selection and regularization: subset selection, shrinkage methods, dimension reduction methods, considerations in high dimensions; Nonlinear regression: polynominal regression, regression splines, smoothing splines, local regression and generalized additive models; Tree-based methods: decision trees, bagging, random forests, and boosting; Support vector machines: maximal margin classifier, support vector classifiers, support vector machines (SVMs), SVMs with more than two classes; Unsupervised learning: principal component analysis, clustering methods. Lectures, 3 hours per week; Lab, 1 hour per week Restriction: open to Data Sciences and Analytics majors only Prerequisite(s): one of STAT 3P82 and STAT 3P86 or STAT (DASA ) 3P87 or permission of the instructor
- Prerequisite(s): one of STAT 3P82 and STAT 3P86 or STAT (DASA ) 3P87 or permission of the instructor
- Restriction: open to Data Sciences and Analytics majors only
來源與參考
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來源參考 : https://brocku.ca/webcal/2024/undergrad/math.html