3.2 Machine learning algorithms

ML Methods
• Instance-based algorithm
• Regression analysis
• Regularization algorithm
• Classifiers
Machine learning library
• Deeplearning4j
• Theano
• Scikit-learn
ML Algorithms
• Decision tree learning
• Association rule learning
• Artificial neural networks
– Deep learning
• Inductive logic programming
• Support vector machines
• Clustering
• Bayesian networks
• Reinforcement learning
• Representation learning
• Similarity and metric learning
• Sparse dictionary learning
• Genetic algorithms
• Rule-based ML
• Linear Poisson modeling
Decision tree learning uses a decision tree as a predictive model, which maps observations about an item to conclusions about the item's target value.
Association rule learning is a method for discovering interesting relations between variables in large databases.
Artificial neural networks (ANN)
• Computations are structured in terms of an interconnected group of artificial neurons, processing information using a connectionist approach to computation.
Deep learning consists of multiple hidden layers in an ANN.
• This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.
Instance-based algorithms
• K-nearest neighbors algorithm (KNN)
• Learning vector quantization (LVQ)
• Self-organizing map (SOM)
Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses.
Support vector machines (SVMs)
• SVMs are a set of related supervised learning methods used for classification and regression.
• Given a set of related training examples, SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.
Cluster analysis is the assignment of a set of observations into clusters according to some pre-designated criteria.
– Clustering is a method of unsupervised learning, and a common technique for statistical data analysis.
Cluster analysis methods
– BIRCH
– DBSCAN
– Expectation-maximization (EM)
– Fuzzy clustering
– Hierarchical Clustering
– K-means algorithm
– K-means clustering
– K-medians
– Mean-shift
– OPTICS algorithm
Unsupervised learning
• Expectation-maximization algorithm
• Vector Quantization
• Generative topographic map
• Information bottleneck method
Ensemble learning
A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG).
Reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward.
Representation learning algorithms attempt to preserve the information in their input but transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions, allowing reconstruction of the inputs coming from the unknown data generating distribution.
Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply, knowledge.
• Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems.