Machine Learning

Machine Learning

1. Supervised

   • try to model relationships and dependencies between the (target) output and the input features
   • Linear Regression
   • Nearest Neighbor
   • task driven
   • Naive Bayes
   • Decision Trees
   • Support Vector Machines
   • Neural Networks
     • Convolution NN
       • (images)
     • Recurrent NN
     • Deep NN
     • Progressive NN
   • labelled data
   • predictive model
   • regression problems
     • Predict a continuous numerical value
   • classification problems
     • Assign a label

2. Unsupervised

   • unlabeled data
   • descriptive model
   • k-means clustering
   • Associations rules
   • Dimensionality Reduction
   • Recommendation

3. Semi-supervised

   • Even though the group memberships of the unlabeled data are unknown, this data carries important information about the group parameters

4. Reinforcement

   • Reward Maximization
   • the agent learns from its experiences of the environment until it explores the full range of possible states
   • Q-Learning
   • Temporal Difference (TD)
   • Deep Adversarial Networks

5. Gives computers the ability to learn without being explicitly programmed