课程目录
- Introduction - Probabilistic and Statistical Machine Learning 2020
- Part 1 - Machine learning and inductive bias
- Part 2 - Warmup- The kNN Classifier
- Part 3 - Formal setup risk consistency
- Part 4 - Bayesian decision theory
- Part 5 - The Bayes classifier
- Part 6 - Risk minimization approximation and estimation error
- Part 7 - Linear least squares
- Part 7a - Introduction to convex optimization
- Part 8 - Feature representation
- Part 9 - Ridge regression
- Part 10 - Lasso
- Part 11 - Cross validation
- Part 12 - Risk minimization vs. probabilistic approaches
- Part 13 - Linear discriminant analysis
- Part 14 - Logistic regression
- Part 15 - Convex optimization Lagrangian dual pro
- Part 16 - Support vector machines- hard and soft margin
- Part 17 - Support vector machines- the dual problem
- Part 18 - Kernels- definitions and examples
- Part 19 - The reproducing kernel Hilbert space
- Part 20 - Kernel SVMs
- Part 21 - Kernelizing least squares regression
- Part 22 - How to center and normalize in feature sp
- Part 23a - Random forests- building the trees
- Part 23b - Random forests- building the forests
- Part 24 - Boosting
- Part 25 - Principle Component Analysis
- Part 26 - Kernel PCA
- Part 27 - Multidimensional scaling
- Part 28 - Random projections and the Theorem of Johnson-Lindenstrauss
- Part 29 - Neighborhood graphs
- Part 30 - Isomap
- Part 31 - t-SNE
- Part 32 - Introduction to clustering
- Part 33 - k-means clustering
- Part 34 - Linkage algorithms for hierarchical cluster
- Part 35 - Spectral graph theory
- Part 36 - Spectral clustering unnormalized case
- Part 37 - Spectral clustering- normalized regularized
- Part 38 - Statistical learning theory- Convergence
- Part 39 - Statistical learning theory- finite function classes
- Part 40 - Statistical learning theory- shattering coefficient
- Part 41 - Statistical learning theory- VC dimension
- Part 42 - Statistical learning theory- Rademacher complexity
- Part 43 - Statistical learning theory- consistency of regularization
- Part 44 - Statistical learning theory- Revisiting Occam and outlook
- Part 45 - ML and Society- The general debate
- Part 46 - ML and Society- (Un)fairness in ML
- Part 47 - ML and Society- Formal approaches to fair
- Part 48 ML and Society Algorithmic approaches to fairness
- Part 49 ML and Society Explainable ML
- Part 50 ML and Society The energy footprint of ML
- Part 51 - Low rank matrix completion- algorithms
- Part 52 - Low rank matrix completion- theory
- Part 53 - Compressed sensing
- Part 54 - ML pipeline- data preprocessing learning
- Part 55 - ML pipeline- evaluation
课程介绍
统计学习是关于计算机基于数据构建的概率统计模型并运用模型对数据进行预测和分析的一门科学，统计学习也称为统计机器学习。
前言：机器学习比力重要的几部分：线性模型、统计学习、深度学习，线性部分包罗SVM、压缩感知、稀疏编码，都是控制整个模型的稀疏性去做线性函数，偏 Discriminative 判别模型；统计学习主要通过统计方法对数据建模找到极大似然，偏 Generative 生成方法；深度学习就是 neural model，偏非线性。
机器学习中的统计多是基于对事件的不确定性度量关于，是主不雅观的，而不是客不雅观地基于频次。
