Questions tagged [machine-learning]

Machine learning revolves around developing self-learning computer algorithms that function by virtue of discovering patterns in data and making intelligent decisions based on such patterns.

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the construction and study of algorithms that can learn from and make predictions about data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions rather than following strictly static program instructions.

NOTE: If you want to use this tag for a question not directly concerning implementation, then consider posting on Cross Validated, Data Science, or Artificial Intelligence instead; otherwise you're probably off-topic. Please choose one site only and do not cross-post to more than one - see Is cross-posting a question on multiple Stack Exchange sites permitted if the question is on-topic for each site? (tl;dr: no).

Classic Problems:

• Classification (supervised learning) classificationsupervised-learning
• Regression (supervised learning) regression
• Clustering (unsupervised learning) cluster-analysisunsupervised-learning
• Density estimation
• Sampling
• Reinforcement Learning reinforcement-learning

Relevant Algorithms:

• Principal component analysis (PCA) pca
• Artificial neural networks (ANN) neural-network
• Support vector machines (SVM) svm support-vector-machines
• K-nearest neighbor (kNN) knn nearest-neighbor
• k-means k-means
• Bayesian networks bayesian-networks
• Gaussian mixture model (GMM) mixture-model
• Decision trees decisiontrees
• Genetic algorithms genetic-algorithm
• Simulated annealing simulated-annealing
• Hidden Markov model (HMM) hidden-markov-models
• Conditional Random Field (CRF)
• Gaussian Processes gaussian-process
• Kalman filter kalman kalman-filter
• Particle filter particle-filter
• Gibbs sampling
• Graphical models
• Ensemble methods (bagging, boosting, ...) ensemble-learning
• Deep learning deep-learning
• Q-Learning q-learning

Applications:

• Computer vision (e.g, object tracking, gesture recognition) computer-vision
• Image recognition (e.g, face, gait, iris, handwriting) image-recognition face-recognition ocr
• Speech recognition speech-recognition
• Speaker recognition voice-recognition
• Natural language processing (NLP) nlp
• Music information retrieval (MIR)
• Bioinformatics bioinformatics
• Spam filtering spam-filtering
• Anomaly detection anomaly-detection
• Automatic vehicle driving
• Recommendation system recommendation-engine
• Machine translation machine-translation

Software:

• LibSVM libsvm
• Weka weka
• Orange orange
• Shogun shogun
• scikit-learn scikit-learn
• PyBrain pybrain
• Apache Mahout mahout
• RapidMiner rapidminer
• KNIME knime
• Waffles
• Azure Machine Learning azure-machine-learning
• nltk nltk
• Caffe caffe
• TensorFlow tensorflow
• Theano theano
• Keras keras
• OpenNMT opennmt
• XGBoost xgboost
• CatBoostcatboost
• Stanford CoreNLP stanford-nlp

Related-tags:

• supervised-learning
• unsupervised-learning
• deep-learning
• reinforcement-learning
• computer-vision
• neural-network
• robotics
• artificial-intelligence
• automation
• classification
• transformer-model, especially gpt-3

Video Lectures:-

• Machine Learning with Python