data-science-advanced-math

# Advanced Mathematics, Algebra, and Optimization for Machine Learning This skill establishes the rigorous mathematical and computational foundations for statistical modeling, Deep Learning architectures, convex/non-convex optimization, and high-dimensional analysis. --- ## 📐 1. Advanced Linear Algebra and Matrix Factorizations for ML ### 1.1 Singular Value Decomposition (SVD) For any real matrix $\mathbf{A} \in \mathbb{R}^{m \times n}$: $$\mathbf{A} = \mathbf{U} \mathbf{\Sigma} \mathbf{V}^T = \sum_{i=1}^r \sigma_i \mathbf{u}_i \mathbf{v}_i^T$$ - $\mathbf{U} \in \mathbb{R}^{m \times m}$ and $\mathbf{V} \in \mathbb{R}^{n \times n}$ are orthogonal matrices ($\mathbf{U}^T\mathbf{U} = \mathbf{I}, \mathbf{V}^T\mathbf{V} = \mathbf{I}$). - $\mathbf{\Sigma} = \text{diag}(\sigma_1, \sigma_2, \dots, \sigma_r, 0, \dots, 0)$ with ordered singular values $\sigma_1 \ge \sigma_2 \ge \dots \ge \sigma_r > 0$. - **Eckart-Young-Mirsky Theorem (Low-Rank Approximation)**: The best rank-$k$ approximation with $k < r$ in Frobenius or Spectral norm is: $$\mathbf{A}_k = \sum_{i=1}^k \sigma_i \mathbf{u}_i \mathbf{v}_i^T, \quad \|\mathbf{A} - \mathbf{A}_k\|_F = \sqrt{\sum_{i=k+1}^r \sigma_i^2}$$ ### 1.2 Moore-Penrose Pseudo-Inverse ($\mathbf{A}^+$) $$\mathbf{A}^+ = \mathbf{V} \mathbf{\Sigma}^+ \mathbf{U}^T$$ Solves linear least squares problems $\min_{\mathbf{x}} \|\mathbf{A}\mathbf{x} - \mathbf{b}\|_2^2$ with the minimum-norm solution $\mathbf{x}^* = \mathbf{A}^+ \mathbf{b}$. ### 1.3 Cholesky and QR Factorizations - **Cholesky ($\mathbf{A} = \mathbf{L}\mathbf{L}^T$)**: For symmetric positive definite (SPD) matrices, computationally twice as fast as LU decomposition. Essential for Gaussian Processes and MCMC sampling. - **QR ($\mathbf{A} = \mathbf{Q}\mathbf{R}$)**: Factors into an orthogonal matrix $\mathbf{Q}$ and an upper triangular matrix $\mathbf{R}$, providing maximum numerical stability for regressions. --- ## 📈 2. Multivariable Differential Calculus and Tensor Calculus ### 2.1 Gradient Vector, Jacobian Matrix, and Hessian Given a scalar field $f: \mathbb{R}^n \to \mathbb{R}$ and a vector function $\mathbf{F}: \mathbb{R}^n \to \mathbb{R}^m$: - **Gradient Vector**: $\nabla f(\mathbf{x}) = \left[ \frac{\partial f}{\partial x_1}, \dots, \frac{\partial f}{\partial x_n} \right]^T \in \mathbb{R}^n$ - **Jacobian Matrix**: $\mathbf{J}_{\mathbf{F}}(\mathbf{x}) = \left[ \frac{\partial F_i}{\partial x_j} \right]_{m \times n} \in \mathbb{R}^{m \times n}$ - **Hessian Matrix (2nd-Order Curvature)**: $\mathbf{H}_f(\mathbf{x}) = \left[ \frac{\partial^2 f}{\partial x_i \partial x_j} \right]_{n \times n} \in \mathbb{R}^{n \times n}$ ### 2.2 Second-Order Multivariable Taylor Series $$f(\mathbf{x} + \Delta\mathbf{x}) \approx f(\mathbf{x}) + \nabla f(\mathbf{x})^T \Delta\mathbf{x} + \frac{1}{2} \Delta\mathbf{x}^T \mathbf{H}_f(\mathbf{x}) \Delta\mathbf{x}$$ ### 2.3 Tensor Chain Rule and Backpropagation For composite functions in computational graphs: $$\frac{\partial L}{\partial \mathbf{W}^{(l)}} = \left( \frac{\partial \mathbf{z}^{(l)}}{\partial \mathbf{W}^{(l)}} \right)^T \frac{\partial L}{\partial \mathbf{z}^{(l)}} = \boldsymbol{\delta}^{(l)} (\mathbf{a}^{(l-1)})^T \quad \text{where } \boldsymbol{\delta}^{(l)} = (\mathbf{W}^{(l+1)})^T \boldsymbol{\delta}^{(l+1)} \odot \sigma'(\mathbf{z}^{(l)})$$ --- ## 🎯 3. Convex Optimization and Duality Theory (Gallier & Quaintance) ### 3.1 Standard Constrained Optimization Problem $$\min_{\mathbf{x} \in \mathbb{R}^n} f_0(\mathbf{x}) \quad \text{subject to } f_i(\mathbf{x}) \le 0 \; (i=1,\dots,m), \quad h_j(\mathbf{x}) = 0 \; (j=1,\dots,p)$$ ### 3.2 Karush-Kuhn-Tucker (KKT) Conditions If the problem is convex and satisfies the constraint qualification condition (Slater's condition), the following conditions are necessary and sufficient for optimality $(\mathbf{x}^*, \boldsymbol{\lambda}^*, \boldsymbol{\nu}^*)$: 1. **Stationarity**: $\nabla f_0(\mathbf{x}^*) + \sum_{i=1}^m \lambda_i^* \nabla f_i(\mathbf{x}^*) + \sum_{j=1}^p \nu_j^* \nabla h_j(\mathbf{x}^*) = \mathbf{0}$ 2. **Primal Feasibility**: $f_i(\mathbf{x}^*) \le 0 \; (\forall i)$ and $h_j(\mathbf{x}^*) = 0 \; (\forall j)$ 3. **Dual Feasibility**: $\lambda_i^* \ge 0 \; (\forall i=1,\dots,m)$ 4. **Complementary Slackness**: $\lambda_i^* f_i(\mathbf{x}^*) = 0 \; (\forall i=1,\dots,m)$ --- ## ⚡ 4. First- and Second-Order Numerical Optimizers ### 4.1 Adam / AdamW Family (First Order with Adaptive Moments) $$\mathbf{m}_t = \beta_1 \mathbf{m}_{t-1} + (1 - \beta_1) \mathbf{g}_t, \quad \mathbf{v}_t = \beta_2 \mathbf{v}_{t-1} + (1 - \beta_2) \mathbf{g}_t^2$$ $$\hat{\mathbf{m}}_t = \frac{\mathbf{m}_t}{1 - \beta_1^t}, \quad \hat{\mathbf{v}}_t = \frac{\mathbf{v}_t}{1 - \beta_2^t}$$ - **AdamW (Decoupled Weight Decay)**: $$\boldsymbol{\theta}_{t+1} = \boldsymbol{\theta}_t - \eta \left( \frac{\hat{\mathbf{m}}_t}{\sqrt{\hat{\mathbf{v}}_t} + \epsilon} + \lambda \boldsymbol{\theta}_t \right)$$ ### 4.2 Second-Order and Quasi-Newton Methods (BFGS and L-BFGS) - **Pure Newton Method**: $\Delta\mathbf{x} = - \mathbf{H}_f^{-1}(\mathbf{x}) \nabla f(\mathbf{x})$ (prohibitive $\mathcal{O}(n^3)$ cost for neural networks). - **L-BFGS (Limited-memory BFGS)**: Keeps only the last $m$ displacement vectors $\mathbf{s}_k = \mathbf{x}_{k+1} - \mathbf{x}_k$ and gradient differences $\mathbf{y}_k = \mathbf{g}_{k+1} - \mathbf{g}_k$, computing the descent direction in $\mathcal{O}(mn)$ time and $\mathcal{O}(mn)$ space. --- ## 🌐 5. Iterative Methods for Large-Scale Linear Systems (Solomon) ### 5.1 Conjugate Gradient (CG) Method Solves $\mathbf{A}\mathbf{x} = \mathbf{b}$ for symmetric positive definite (SPD) matrices in at most $n$ iterations, generating $\mathbf{A}$-orthogonal directions ($\mathbf{p}_i^T \mathbf{A} \mathbf{p}_j = 0$ for $i \ne j$): $$\alpha_k = \frac{\mathbf{r}_k^T \mathbf{r}_k}{\mathbf{p}_k^T \mathbf{A} \mathbf{p}_k}, \quad \mathbf{x}_{k+1} = \mathbf{x}_k + \alpha_k \mathbf{p}_k, \quad \mathbf{r}_{k+1} = \mathbf{r}_k - \alpha_k \mathbf{A} \mathbf{p}_k$$ $$\beta_k = \frac{\mathbf{r}_{k+1}^T \mathbf{r}_{k+1}}{\mathbf{r}_k^T \mathbf{r}_k}, \quad \mathbf{p}_{k+1} = \mathbf{r}_{k+1} + \beta_k \mathbf{p}_k$$ --- ## 🗺️ 6. Topology, Manifolds, and Non-Linear Dimensionality Reduction ### 6.1 Manifold Learning - **Isomap**: Preserves geodesic distances computed on $k$-nearest-neighbor ($k$-NN) graphs via Dijkstra + Classical Multidimensional Scaling (MDS). - **t-SNE (t-Distributed Stochastic Neighbor Embedding)**: Models neighborhood probabilities in the high-dimensional space with a Gaussian distribution and in the low-dimensional space with a Student's t-distribution (1 degree of freedom), minimizing the Kullback-Leibler divergence: $$\text{KL}(P \parallel Q) = \sum_i \sum_j p_{j|i} \log \frac{p_{j|i}}{q_{j|i}}$$ - **UMAP (Uniform Manifold Approximation and Projection)**: Based on Riemannian geometry and algebraic topology (Fuzzy Simplicial Sets), guaranteeing preservation of both local and global structure. --- ## 🎲 7. Statistical Inference and Regularization ### 7.1 L1, L2, and Elastic Net Regularization $$\mathcal{L}_{\text{ElasticNet}}(\mathbf{w}) = \frac{1}{2n} \|\mathbf{y} - \mathbf{X}\mathbf{w}\|_2^2 + \lambda \left( \alpha \|\mathbf{w}\|_1 + \frac{1 - \alpha}{2} \|\mathbf{w}\|_2^2 \right)$$ - **L1 (Lasso, $\alpha=1$)**: Promotes sparsity by driving exact weights to zero due to the corners of the $L_1$ norm polytope. - **L2 (Ridge, $\alpha=0$)**: Prevents multicollinearity and smooths the coefficient norm.