Conclusion
Topic Modeling
Topic modeling is a prevalent unsupervised learning problem. You have a collection of text documents, and you would like to discover topics present in each document. Latent Dirichlet Allocation (LDA) is a very effective algorithm of topic discovery.
Gaussian Processes
Gaussian processes (GP) is a supervised learning method that competes with kernel regression. It has some advantages over the latter. For example, it provides confidence intervals for the regression line in each point.
Generalized Linear Models
Generalized linear model (GLM) is a generalization of the linear regression to modeling various forms of dependency between the input feature vector and the target. Logistic regression, for instance, is one form of GLMs.
Probabilistic graphical Models
Markov Chain Monte Carlo
Markov chain Monte Carlo is a class of algorithms for sampling from any probability distribution defined mathematically. Sampling from standard distributions, such as normal or uniform, is relatively easy because their properties are well known. However, the task of sampling becomes significantly more complicated when the probability distribution can have an arbitrary form defined by a dependency graph learned from data.
Genetic Algorithms
Genetic algorithms (GA) are a numerical optimization technique used to optimize undifferentiable optimization objective functions. They use concepts from evolutionary biology to search for a global optimum (minimum or maximum) of an optimization problem, by mimicking evolutionary biological processes.
GA work by starting with an initial generation of candidate solutions. If we look for optimal values of the parameters of our model, we first randomly generate multiple combinations of parameter values. We then test each combination of parameter values against the objective function. Imagine each combination of parameter values as a point in a multi-dimensional space. We then generate a subsequent generation of points from the previous generation by applying such concepts as “selection,” “crossover,” and “mutation.”.
Reinforcement learning
Reinforcement learning (RL) solves a very specific kind of problems where the decision making is sequential. Usually, there’s an agent acting in an unknown environment. Each action brings a reward and moves the agent to another state of the environment (usually, as a result of some random process with unknown properties). The goal of the agent is to optimize its long-term reward. Reinforcement learning algorithms, such as Q-learning, as well as its neural network based counterparts, are used in learning to play video games, robotic navigation and coordination, inventory and supply chain management, optimization of complex electric power systems (power grids), and learning financial trading strategies.