Machine Learning and Deep Learning

Statistical Learning? > `Predict target data using Observation or Given Data` $$ \textcolor{blue}{Sales} = f(TV, Radio, Newspaper) $$ $$\textcolor{blue}{Sales}$$ : `Target Variable, Response Variable...

Linear regression is a simple approach to supervised learning. It assumes that the dependence of $Y$ on $X_1, X_2, \\cdots , X_p$ is linear.True regre

Classification Qualitative variables take values in an unordered set $C$, such as $$ \text{eye color}\in\{\text{{brown, blue, green}}\}\\ \text{email}

Cross-validation and the Bootstrap In the section we discuss two resampling methods Cross-validation Bootstrap These methods refit a model of inte

Often the linearity assumption is good enoughWhen its not...PolynomialsStep functionsSplinesLocal regressionGeneralized additive modelsOffer a lot of

Here we describe tree-based methods for regression and classificationThese involve stratifying or segmenting the predictor space into a number of simp

This session focuses on multiple hypothesis testingA single null hypothesis might look like$\\mathcal{H}\_0$ : the expected blood pressures of mice in

Here we approach the two-class classification problem in a direct way :We try and find a plane that seperates the classes in feature spaceIf we cannot

Survival analysis concerns a special kind of outcome variable : the time until an event occursFor example, suppose that we have conducted a five-year

Most of this course focuses on supervised learning methods such as regression and classificationIn that setting we observe obth a set of features $X_1

$$f(X)=\\beta0+\\sum^K{k=1}\\betakh_k(X)\\0.2cm=\\beta_0+\\sum^K{k=1}\\betak g(w{k0}+\\sum^p{j=1}w{kj}X_j)$$$Ak=h_k(X)=g(w{k0}+\\sum^p{j=1} w{kj}X_j)$