We found 9 relevant roadmaps
  1. Bayesian machine learning

    author: Roger Grosse

    An overview of Bayesian machine learning techniques.

  2. Differential geometry for machine learning

    author: Roger Grosse

    An overview of several uses of differential geometry ideas in machine learning

  3. Coursera: Machine Learning

    author: Colorado Reed

    A supplement to Andrew Ng's Coursera machine learning course

  4. Dynamical Systems for Machine Learning

    author: Daniel Jiwoong Im

    An overview of taking dynamical systems approaches to learning.

  5. Stanford CS229: Machine Learning

    author: Roger Grosse

    CS229 is Stanford's graduate course in machine learning, currently taught by Andrew Ng. It provides an overview of techniques for supervised, unsupervised, and reinforcement learning, as well as some results from computational learning theory.

  6. Level-Up Your Machine Learning

    author: Colorado Reed

    This roadmap provides an answer to the question: How can I "get better" at machine learning when I don't know what I should study?

  7. Deep learning from the bottom up

    author: Roger Grosse

    An overview of key ideas and recent advances in deep learning.

  8. Automation Anywhere: Utilizing AI And RPA To Thrive Better

    author: igmGuru

  9. Decision Stream

    author: Prof. Kee

    Decision stream - a supervised machine learning technique providing classification with recursive execution of two procedures: partitioning data into statistically different samples and merging the samples which are similar according to the test statistics

We found 15 relevant concepts
  1. K nearest neighbors

    K nearest neighbors is a very simple machine learning algorithm which simply averages the labels of the K nearest neighbors in the training set. It is a canonical example of a nonparametric learning algorithm. It has the advantage that it can learn arbitrarily complex functions, but it is especially sensitive to the curse of dimensionality.
  2. restricted Boltzmann machines

    Restricted Boltzmann machines (RBMs) are a type of undirected graphical model typically used for learning binary feature representations. The structure consists of a bipartite graph with a layer of visible units to represent the inputs and a layer of hidden units to represent more abstract features. Training is intractable, but approximations such as contrastive divergence work well in practice. RBMs are a building block of many models in deep learning.
  3. multiple integrals

    A multiple integral generalizes integration to functions of n variables and produces a general (n-1)-dimensional volume. For instance, n=2 corresponds to an area. Multiple integrals occur frequently in probability theory and machine learning when examining marginal densities.
  4. early stopping

    Early stopping is a technique for controlling overfitting in machine learning models, especially neural networks, by stopping training before the weights have converged. Often we stop when the performance has stopped improving on a held-out validation set.
  5. Bayesian parameter estimation: Gaussian distribution

    Using the Bayesian framework, we can infer the mean parameter of a Gaussian distribution, the scale parameter, or both. Since Gaussians are widely used in probabilistic modeling, the computations that go into this are common motifs in Bayesian machine learning more generally.
  6. loss function

    A loss function or cost function is a function that maps the outcome of a decision to a real-valued cost associated with that outcome. Loss functions are common in machine learning, information theory, statistics, and mathematical optimization, and help guide decision making under uncertainty.
  7. random forests

    Random forests are a machine learning algorithm which averages the predictions over decision trees restricted to random subsets of the input features. They are widely used because they often perform very well with almost no parameter tuning.
  8. constructing kernels

    The kernel trick allows us to reformulate linear machine learning models in terms of a kernel function which defines a notion of similarity between data points. A few simple rules allow us to construct kernels which capture a wide variety of similarity functions.
  9. covariance matrices

    A covariance matrix generalizes the idea of variance to multiple dimensions, where the i-th j-th element in the covariance matrix is the covariance between the i-th and j-th random variables. Covariance matrices are common throughout both statistics and machine learning and often arise when dealing with multivariate distributions.
  10. LASSO

    The Lasso is a form of regularized linear regression. Unlike ridge regression, it puts an L1 penalty on the weights, which encourages sparsity, i.e. it encourages most of the weights to be exactly zero. The general trick of using L1 norms to encourage sparsity is widely used in machine learning.
  11. logistic regression

    Logistic regression is a machine learning model for binary classification, i.e. learning to classify data points into one of two categories. It's a linear model, in that the decision depends only on the dot product of a weight vector with a feature vector. This means the classification boundary can be represented as a hyperplane. It's a widely used model in its own right, and the general structure of linear-followed-by-sigmoid is a common motif in neural networks.