Kernelizing Perceptron

Decision Tree Learning

Margins

Naive Bayes

Maximizing Conditional Likelihood

Kernelizing Algorithms

Primal and Dual Forms

Finite Hypothesis Classes

k-fold cross-validation

Neural network

Convolutional neural network (CNN)

Objective-Based Clustering

Principal Component Analysis (PCA)

Semi-Supervised Learning

Learning Linear Separators

Maximum Likelihood Estimation (MLE)

Bag of Words

Computer Vision

Generalization and Overfitting

Model Selection and Regularization

Support Vector Machine (SVM)

Markov Decision Process (MDP)

Minimizing Squared Error

Deep Networks

Adaboost

Learning Representations

Active Learning

Co-training

Q-learning

Overfitting

Conditional Independence

Gradient descent

Kernelizing SVM

VC Dimension Based Bounds

Linear regression

Backpropagation

Boosting

Hierarchical Clustering

Interactive Learning

Transductive SVM

Value iteration

Bayes' theorem

Perceptron

Maximum A Posteriori (MAP)

Logistic Regression

Kernel (operating system)

Geometric Margins

Sample Complexity

Structural Risk Minimization

Maximizing Data Likelihood

Unsupervised learning

Dimensionality Reduction

Sampling Bias

Reinforcement learning (RL)

Convolution

10-401 Introduction to Machine Learning Carnegie Mellon University Spring 2018 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.   - Machine Learning, Tom Mitchell. (optional)
- Pattern Recognition and Machine Learning, Christopher Bishop. (optional)
- Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, [available online](https://ebookcentral.proquest.com/lib/cm/detail.action?docID=3339490), (optional)

10-401 Introduction to Machine Learning

Machine Learning

Kernel perceptron

Kernelizing Perceptron

1 courses cover this concept

10-401 Introduction to Machine Learning