creation of obsidian vault and first notes

ObsidianNotes/.obsidian/app.json

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ObsidianNotes/.obsidian/appearance.json

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ObsidianNotes/.obsidian/core-plugins.json

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ObsidianNotes/elemlds lecture 8.md

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+# Machine Learning Intro
+
+= Machines that *learn* to perform a task from *experience*
+
+3 forms of learning based on labels availability:
+- Yes -> Supervised learning
+- Some -> Semi-supervised learning
+- No -> Unsupervised learning
+
+# Supervised Learning
+Training data has labels $\mathcal{D} = \{(x_1, t_1), \dots, (x_N, t_N\}$
+Goal: learn a *predictive* function that yields good performance on *unseen* data
+
+Data may need to be preprocessed to handle
+- Missing/wrong values
+- Outliers
+- Inconsistencies
+
+# Features
+Feature extraction = process that creates descriptive vectors from samples
+- Features should be invariant to irrelevant input variations
+- Selecting the *right* features!
+- Usually encode some domain knowledge
+- Higher-dimensional features are more discriminative
+
+Curse of dimensionality: complexity increases *exponentially*  with number of dimensions
+
+# Terms, Concepts, Notation
+Mostly based on statistics and probability theory
+Notation:
+- Scalar $x \in \mathbb{R}$
+- Vector-valued $\text{x} \in \mathbb{R}$
+- Datasets $\mathcal{X} \in \mathbb{R}$
+- Labelled datasets $\mathcal{D}  = \{(x_1, t_1), \dots, (x_N, t_N\}$
+- Matrices  $\text{M} \in \mathbb{R}^{m \times n}$
+- Dot product $\text{w}^\text{T}\text{x} = \sum_{j=1}^D w_j x_j$
+
+# Probability Basics
+Over random variables:
+- Discrete case: $p(X = x_j) = \frac{n_j}{N}$
+- Continuous case: $p(X \in (x_1, x_2)) = \int_{x_1}^{x_2}p(x)\, dx$ where $p(x)$ is the probability desnity function (pdf) of $x$
+
+Some formulas:
+Let $A \in \{a_i\}, B \in \{b_j\}$
+Consider $N$ trials:
+- $n_{ij} = \# \{A = a_i \land B =b_j\}$
+- $c_i = \#\{A=a_i\}$
+- $r_j = \#\{B=b_j\}$
+Then we get:
+- Joint probability $p(A=a_i, B=b_j) = \frac{n_{ij}}{N}$
+- Marginal probability $p(A=a_i) = \frac{c_i}{N}$
+- Conditional probability $P(B=b_j | A=a_i)=\frac{n_{ij}}{c_i}$
+- Sum rule $p(A=a_i) = \frac{1}{N}\sum_j n_{ij} = \sum_{b_j}p(A=a_i,B=b_j)$
+- Product rule $P(A=a_i, B =b_j) = \frac{n_{ij}}{c_i}\cdot \frac{c_i}{N} = p(B=b_j |A=a_i)\cdot p(A=a_i)$
+
+In short:
+- Sum rule: $p(A) = \sum_Bp(A,b)$
+- Product rule: $p(A,B) = p(B|A)p(A)$
+- Bayes' Theorem: $p(A|B)= \frac{p(B|A)p(A)}{\sum_Ap(B|A)p(A)}$
+

ObsidianNotes/elemlds lecture 9.md

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+# Bayes Decision Theory
+Goal: predict output class $\mathcal{C}$ from measurements $\text{x}$ by minimizing the probability of misclassification
+
+>[!tip] Main Equation:
+>$$p(X,Y)=\frac{p(X|Y)p(Y)}{p(X)}$$
+
+