Spring 2018

Carnegie Mellon University

A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.

Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as decision tree learning, Support Vector Machines, neural networks, boosting, statistical learning methods, unsupervised learning, active leaerning, and reinforcement learning. Short programming assignments include hands-on experiments with various learning algorithms.

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- Machine Learning, Tom Mitchell. (optional)
- Pattern Recognition and Machine Learning, Christopher Bishop. (optional)
- Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, available online, (optional)

Lecture slides available at Lectures

No videos available

Homework available at Homeworks

Readings and links available at Lectures

Recitations available at Recitations

Course project guidelines available at Project

Active LearningAdaboostBackpropagationBag of WordsBayes' theoremBoostingCo-trainingComputer VisionConditional IndependenceConvolutionConvolutional neural network (CNN)Decision Tree LearningDeep NetworksDimensionality ReductionFinite Hypothesis ClassesGeneralization and OverfittingGeometric MarginsGradient descentHierarchical ClusteringInteractive LearningKernel (operating system)Kernelizing AlgorithmsKernelizing PerceptronKernelizing SVMLearning Linear SeparatorsLearning RepresentationsLinear regressionLogistic RegressionMarginsMarkov Decision Process (MDP)Maximizing Conditional LikelihoodMaximizing Data LikelihoodMaximum A Posteriori (MAP)Maximum Likelihood Estimation (MLE)Minimizing Squared ErrorModel Selection and RegularizationNaive BayesNeural networkObjective-Based ClusteringOverfittingPerceptronPrimal and Dual FormsPrincipal Component Analysis (PCA)Q-learningReinforcement learning (RL)Sample ComplexitySampling BiasSemi-Supervised LearningStructural Risk MinimizationSupport Vector Machine (SVM)Transductive SVMUnsupervised learningVC Dimension Based BoundsValue iterationk-fold cross-validation