Short Answer * Expectation Maximization (EM) is an iterative algorithm for finding the maximum a posteriori (MAP) estimated of parameters in statistical modles, where the model depends on unobserved latent variables. * The algorithm iteratively repeats two steps until convergence or until a maximum number of iteratively repeats two steps until convergence
* A support-vector machine (SVM) constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks like outlier detection. * A good separtion is achieved by the hyperplane that has the largest distance to the nearest training-data point of any
Short Answer * LARS (least angle regression) is an algorithm for computing linear regression with \(l_{1}\) regularization stepwisely. * The basic steps of the LARS algorithm are: * Start with all coefficients \( \beta \) equal to zero. * Find the feature \(x_j\) most correlated with the response \(y\). * Increase the coefficient \(\beta_j\
Short Answer * Isotonic regression is similar to linear regression, except for some monotonic constraints. Additionally, linear regression is global, while isotonic regression is pairwise. Long Answer * Assume we have dataset \(\{(x_{1}, y_{1}), (x_{2}, y_{2}), \ldots, (x_{n}, y_{n})\}\) such that \(x_{1}<x_{2}
Short Answer * Latent semantic analysis (LSA) is a natural language processing technique for analyzing documents and terms contained within them. * Generally speaking, we can procedure LSA in 4 steps: 1. Collect, clean, and prepare text data for further analysis. 2. Build document term matrix from the cleaned text documents. 3.
Short Answer Canopy clustering is a pre-clustering algorithm, which performs clustering in two stages: * Stage 1. Use an "unexpensive" distance measurement to roughly divide the data into overlapping subsets we call "canopies". * State 1. Use an "expensive" distance measurement to perform an elaborate clustering.
* Streaming k-means is a version of k-means where the dataset is fed in a stream rather than all dumped at one time. * To present this concept clearly. We will give a short explanation of k-means first, then we will interpret why streaming data do matter and what shall we change
* There are different approaches and algorithms to perform clustering tasks which can be divided into three sub-categories: * Partition-based clustering: k-means, k-median * Hierarchical clustering: agglomerative, divisive * Density-based clustering: DBSCAN * DBSCAN stands for density-based spatial clustering of applications with noise. We suppose that you have been familiar with k-means clustering. Before plunging
* The recommender system produces a list of recommendations such as movies to see, products to buy, news to read, and so on. Different Categories of Recommender Systems * Recommender systems can be divided into two main types: Content-based and Collaborative filter recommender systems. * Before plunging into the various concepts, let'
* Adaboost stands for Adaptive Boosting where a set of simple and weak classifiers is used to achieve an improved classifier by emphasizing the samples that are misclassified by weak classifiers. * For this concept, Sebastian Raschka & Vahid Mirjalili give a very clear explanation for it in their book Python Machine
* Categorical features usually indicate the type of observation. For instance, "Gender" can have two categories such as 'male' and 'female'. * There are several methods to handle categorical features, such as one-hot-encoding, target encoding, dummy encoding, hash encoding, etc. * One-hot-encoding maps a k-category feature to
