Machine Learning: Course Lengths

This page provides an overview of the number of hours of lecture content that typically devoted to different topics, based on a survey of wide range of courses related to various aspects of machine learning, taken from a broad spectrum of universities around the world. The table below allows the distribution of course hours to be viewed for both high-level topics and their specific subtopics. In each case, a pie chart illustrating the proportion of courses covering a given topic can be viewed, together with a histogram showing the range of hours spent on that topic, which is followed by a list of links to specific courses covering the topic.

Topics Covered (Click on the book icon to view subtopics)	Hours Covered on Average (Click on the + symbol for detailed breakdown)
General concepts	4.0 Hours.
Machine learning, University of British Columbia	6.15 hours
Practical Machine Learning, University of California, Berkeley	5.06 hours
Introduction to Machine Learning, Brown University	5.05 hours
Introduction to Machine Learning, University of Bristol	4.93 hours
Machine Learning, University at Buffalo	4.11 hours
Learning from Data, University of Edinburgh	3.74 hours
Machine Learning, University of Cambridge	2.59 hours
Introduction to Machine Learning, University of Alberta	2.25 hours
Machine Learning, Carnegie Mellon University	1.95 hours
Intro/Overview/Applications	1.3 Hours.
Practical Machine Learning, University of California, Berkeley	2.12 hours
Learning from Data, University of Edinburgh	2.0 hours
Introduction to Machine Learning, Brown University	1.5 hours
Machine Learning, University of Cambridge	1.38 hours
Introduction to Machine Learning, University of Alberta	1.33 hours
Machine learning, University of British Columbia	1.29 hours
Introduction to Machine Learning, University of Bristol	1.0 hours
Machine Learning, Carnegie Mellon University	0.86 hours
Machine Learning, University at Buffalo	0.47 hours
Classifier Performance Estimation	0.8 Hours.
Introduction to Machine Learning, University of Bristol	1.55 hours
Introduction to Machine Learning, Brown University	1.5 hours
Practical Machine Learning, University of California, Berkeley	0.57 hours
Learning from Data, University of Edinburgh	0.16 hours
Machine Learning, University at Buffalo	0.03 hours
Statistical Tests for Classifier Comparison	0.7 Hours.
Introduction to Machine Learning, University of Bristol	1.24 hours
Learning from Data, University of Edinburgh	0.08 hours
ROC Curves	0.5 Hours.
Introduction to Machine Learning, Brown University	1.5 hours
Practical Machine Learning, University of California, Berkeley	0.57 hours
Introduction to Machine Learning, University of Bristol	0.31 hours
Learning from Data, University of Edinburgh	0.08 hours
Machine Learning, University at Buffalo	0.03 hours
Bootstrap	0.9 Hours.
Practical Machine Learning, University of California, Berkeley	0.86 hours
Sufficient statistics	0.0 Hours.
Distance metrics	0.1 Hours.
Machine Learning, Carnegie Mellon University	0.09 hours
Important Distributions	0.9 Hours.
Machine Learning, University at Buffalo	1.96 hours
Machine learning, University of British Columbia	1.62 hours
Introduction to Machine Learning, Brown University	1.3 hours
Machine Learning, University of Cambridge	1.0 hours
Learning from Data, University of Edinburgh	0.52 hours
Introduction to Machine Learning, University of Alberta	0.39 hours
Machine Learning, Carnegie Mellon University	0.34 hours
Practical Machine Learning, University of California, Berkeley	0.15 hours
Gaussian	0.5 Hours.
Machine learning, University of British Columbia	1.0 hours
Machine Learning, University at Buffalo	0.88 hours
Introduction to Machine Learning, Brown University	0.55 hours
Learning from Data, University of Edinburgh	0.52 hours
Machine Learning, University of Cambridge	0.36 hours
Introduction to Machine Learning, University of Alberta	0.25 hours
Machine Learning, Carnegie Mellon University	0.22 hours
Practical Machine Learning, University of California, Berkeley	0.09 hours
Discrete	0.6 Hours.
Machine Learning, University at Buffalo	1.08 hours
Introduction to Machine Learning, Brown University	0.75 hours
Machine learning, University of British Columbia	0.62 hours
Practical Machine Learning, University of California, Berkeley	0.06 hours
Continuous (non Gaussian)	0.6 Hours.
Introduction to Machine Learning, Brown University	0.75 hours
Machine Learning, University at Buffalo	0.74 hours
Practical Machine Learning, University of California, Berkeley	0.18 hours
Curse of dimensionality	0.1 Hours.
Machine Learning, Carnegie Mellon University	0.13 hours
Machine Learning, University at Buffalo	0.08 hours
Learning from Data, University of Edinburgh	0.06 hours
Bayesian Decision theory	0.6 Hours.
Introduction to Machine Learning, University of Bristol	1.0 hours
Machine Learning, University at Buffalo	0.24 hours
Error Estimation	0.3 Hours.
Machine Learning, Carnegie Mellon University	0.52 hours
Machine learning, University of British Columbia	0.38 hours
Introduction to Machine Learning, University of Alberta	0.02 hours
Chernoff's Bound/hoeffding inequality	0.1 Hours.
Machine Learning, Carnegie Mellon University	0.3 hours
Machine learning, University of British Columbia	0.1 hours
Introduction to Machine Learning, University of Alberta	0.02 hours
Bhattacharyya Coefficient	0.0 Hours.
Linear Algebra Review	0.6 Hours.
Learning from Data, University of Edinburgh	1.0 hours
Practical Machine Learning, University of California, Berkeley	0.24 hours
Information Theory	1.9 Hours.
Machine learning, University of British Columbia	6.0 hours
Machine Learning, University of Cambridge	0.64 hours
Machine Learning, University at Buffalo	0.54 hours
Machine Learning, Carnegie Mellon University	0.22 hours
KL Divergence	0.1 Hours.
Machine Learning, University of Cambridge	0.05 hours
Essential Probability theory/Statistics	0.6 Hours.
Introduction to Machine Learning, University of Bristol	1.38 hours
Practical Machine Learning, University of California, Berkeley	0.94 hours
Introduction to Machine Learning, University of Alberta	0.58 hours
Machine Learning, University at Buffalo	0.32 hours
Machine Learning, University of Cambridge	0.16 hours
Machine Learning, Carnegie Mellon University	0.07 hours
Feature separability	0.1 Hours.
Machine Learning, Carnegie Mellon University	0.09 hours
Learning from Data, University of Edinburgh	0.07 hours
Feature Selection/Extraction	1.7 Hours.
Introduction to Machine Learning, University of Alberta	3.07 hours
Machine Learning, University at Buffalo	3.03 hours
Introduction to Machine Learning, Brown University	2.0 hours
Learning from Data, University of Edinburgh	1.75 hours
Practical Machine Learning, University of California, Berkeley	1.75 hours
Machine Learning, University of Cambridge	1.47 hours
Introduction to Machine Learning, University of Bristol	1.0 hours
Machine Learning, Carnegie Mellon University	0.88 hours
Machine learning, University of British Columbia	0.06 hours
Data Preprocessing/normalisation	0.6 Hours.
Learning from Data, University of Edinburgh	0.75 hours
Introduction to Machine Learning, University of Bristol	0.47 hours
Independent Components Analysis	0.6 Hours.
Introduction to Machine Learning, University of Alberta	1.33 hours
Introduction to Machine Learning, Brown University	0.5 hours
Machine Learning, University of Cambridge	0.43 hours
Practical Machine Learning, University of California, Berkeley	0.26 hours
Dimensionality Reduction	1.1 Hours.
Machine Learning, University at Buffalo	1.61 hours
Introduction to Machine Learning, University of Alberta	1.57 hours
Introduction to Machine Learning, Brown University	1.5 hours
Practical Machine Learning, University of California, Berkeley	1.49 hours
Learning from Data, University of Edinburgh	1.28 hours
Machine Learning, Carnegie Mellon University	0.88 hours
Machine Learning, University of Cambridge	0.54 hours
Introduction to Machine Learning, University of Bristol	0.53 hours
Machine learning, University of British Columbia	0.06 hours
Intro/overview	0.2 Hours.
Practical Machine Learning, University of California, Berkeley	0.39 hours
Machine Learning, Carnegie Mellon University	0.06 hours
Principal Components Analysis	0.7 Hours.
Machine Learning, University at Buffalo	1.42 hours
Introduction to Machine Learning, University of Alberta	1.16 hours
Machine Learning, Carnegie Mellon University	0.82 hours
Introduction to Machine Learning, Brown University	0.75 hours
Learning from Data, University of Edinburgh	0.72 hours
Introduction to Machine Learning, University of Bristol	0.53 hours
Machine Learning, University of Cambridge	0.45 hours
Practical Machine Learning, University of California, Berkeley	0.41 hours
Machine learning, University of British Columbia	0.06 hours
Probabilistic PCA	0.4 Hours.
Introduction to Machine Learning, Brown University	0.75 hours
Machine Learning, University of Cambridge	0.09 hours
Linear Discriminant Analysis	0.3 Hours.
Introduction to Machine Learning, University of Alberta	0.41 hours
Practical Machine Learning, University of California, Berkeley	0.28 hours
Machine Learning, University at Buffalo	0.19 hours
Canonical correlation analysis	0.3 Hours.
Practical Machine Learning, University of California, Berkeley	0.28 hours
Non-negative Matrix Factorization	0.1 Hours.
Practical Machine Learning, University of California, Berkeley	0.13 hours
Kernel PCA	0.8 Hours.
Machine Learning, University at Buffalo	1.42 hours
Introduction to Machine Learning, University of Alberta	0.17 hours
Factor Analysis	0.5 Hours.
Machine Learning, University of Cambridge	0.5 hours
Feature Selection	0.5 Hours.
Practical Machine Learning, University of California, Berkeley	0.86 hours
Machine Learning, Carnegie Mellon University	0.08 hours
Clustering	0.8 Hours.
Introduction to Machine Learning, Brown University	2.25 hours
Practical Machine Learning, University of California, Berkeley	1.45 hours
Machine Learning, University at Buffalo	0.72 hours
Machine Learning, Carnegie Mellon University	0.62 hours
Introduction to Machine Learning, University of Bristol	0.13 hours
Machine learning, University of British Columbia	0.06 hours
Learning from Data, University of Edinburgh	0.04 hours
Intro/overview	0.2 Hours.
Practical Machine Learning, University of California, Berkeley	0.2 hours
Similarity Measures/Clustering Criteria	0.3 Hours.
Practical Machine Learning, University of California, Berkeley	0.26 hours
K-Means	0.6 Hours.
Introduction to Machine Learning, Brown University	2.25 hours
Machine Learning, University at Buffalo	0.72 hours
Machine Learning, Carnegie Mellon University	0.62 hours
Practical Machine Learning, University of California, Berkeley	0.59 hours
Introduction to Machine Learning, University of Bristol	0.13 hours
Learning from Data, University of Edinburgh	0.04 hours
Machine learning, University of British Columbia	0.03 hours
Hierarchical	0.1 Hours.
Practical Machine Learning, University of California, Berkeley	0.2 hours
Machine learning, University of British Columbia	0.03 hours
Spectral/Graph-Based	0.2 Hours.
Practical Machine Learning, University of California, Berkeley	0.2 hours
Cluster validity	0.0 Hours.
Competitive Learning/SOMs	0.0 Hours.
Fuzzy clustering	0.0 Hours.
Vector Quantization	0.1 Hours.
Practical Machine Learning, University of California, Berkeley	0.1 hours
Decision Boundaries	5.9 Hours.
Machine Learning, University at Buffalo	12.52 hours
Machine Learning, Carnegie Mellon University	8.07 hours
Introduction to Machine Learning, University of Alberta	6.15 hours
Learning from Data, University of Edinburgh	6.06 hours
Introduction to Machine Learning, Brown University	5.25 hours
Introduction to Machine Learning, University of Bristol	4.12 hours
Practical Machine Learning, University of California, Berkeley	3.88 hours
Machine learning, University of British Columbia	1.54 hours
Overview	0.4 Hours.
Machine learning, University of British Columbia	0.78 hours
Practical Machine Learning, University of California, Berkeley	0.37 hours
Machine Learning, University at Buffalo	0.28 hours
Learning from Data, University of Edinburgh	0.25 hours
Linear Discriminants	1.5 Hours.
Introduction to Machine Learning, Brown University	3.0 hours
Machine Learning, Carnegie Mellon University	1.95 hours
Machine Learning, University at Buffalo	1.6 hours
Learning from Data, University of Edinburgh	1.13 hours
Introduction to Machine Learning, University of Alberta	1.07 hours
Practical Machine Learning, University of California, Berkeley	0.37 hours
Perceptron Learning	0.2 Hours.
Machine Learning, Carnegie Mellon University	0.47 hours
Machine Learning, University at Buffalo	0.22 hours
Introduction to Machine Learning, University of Alberta	0.2 hours
Practical Machine Learning, University of California, Berkeley	0.09 hours
Learning from Data, University of Edinburgh	0.06 hours
Minimum Squared Error	0.1 Hours.
Machine Learning, University at Buffalo	0.16 hours
Introduction to Machine Learning, University of Alberta	0.13 hours
Logistic regression	1.2 Hours.
Introduction to Machine Learning, Brown University	3.0 hours
Machine Learning, Carnegie Mellon University	1.48 hours
Learning from Data, University of Edinburgh	1.07 hours
Machine Learning, University at Buffalo	0.72 hours
Introduction to Machine Learning, University of Alberta	0.44 hours
Practical Machine Learning, University of California, Berkeley	0.28 hours
Bayesian Logistic Regression	0.6 Hours.
Machine Learning, University at Buffalo	0.57 hours
Quadratic Discriminant Functions	0.0 Hours.
Bayes Classifier/Class-conditional densities	0.4 Hours.
Introduction to Machine Learning, University of Bristol	0.78 hours
Machine learning, University of British Columbia	0.67 hours
Machine Learning, University at Buffalo	0.44 hours
Learning from Data, University of Edinburgh	0.37 hours
Machine Learning, Carnegie Mellon University	0.31 hours
Practical Machine Learning, University of California, Berkeley	0.09 hours
Support Vector Machine	1.3 Hours.
Machine Learning, University at Buffalo	2.76 hours
Machine Learning, Carnegie Mellon University	1.84 hours
Introduction to Machine Learning, University of Alberta	1.33 hours
Introduction to Machine Learning, University of Bristol	0.57 hours
Practical Machine Learning, University of California, Berkeley	0.23 hours
Relevance Vector Machine	0.1 Hours.
Machine Learning, University at Buffalo	0.07 hours
Kernel Methods/Kernel Trick	1.0 Hours.
Introduction to Machine Learning, Brown University	1.5 hours
Introduction to Machine Learning, University of Alberta	1.33 hours
Machine Learning, University at Buffalo	1.01 hours
Machine Learning, Carnegie Mellon University	0.77 hours
Practical Machine Learning, University of California, Berkeley	0.21 hours
k-Nearest Neighbour Rule	0.6 Hours.
Learning from Data, University of Edinburgh	1.0 hours
Machine Learning, Carnegie Mellon University	0.6 hours
Introduction to Machine Learning, University of Bristol	0.43 hours
Practical Machine Learning, University of California, Berkeley	0.28 hours
Decision Trees	1.2 Hours.
Machine Learning, University at Buffalo	2.83 hours
Introduction to Machine Learning, University of Bristol	1.62 hours
Machine Learning, Carnegie Mellon University	1.37 hours
Introduction to Machine Learning, University of Alberta	1.33 hours
Introduction to Machine Learning, Brown University	0.75 hours
Practical Machine Learning, University of California, Berkeley	0.33 hours
Machine learning, University of British Columbia	0.09 hours
Multi-class Classification	2.0 Hours.
Practical Machine Learning, University of California, Berkeley	2.0 hours
Neural Networks	1.9 Hours.
Learning from Data, University of Edinburgh	3.31 hours
Machine Learning, University at Buffalo	3.21 hours
Introduction to Machine Learning, University of Alberta	1.24 hours
Machine Learning, Carnegie Mellon University	1.23 hours
Introduction to Machine Learning, University of Bristol	0.72 hours
Multilayer Perceptron	0.6 Hours.
Learning from Data, University of Edinburgh	1.0 hours
Machine Learning, Carnegie Mellon University	0.75 hours
Machine Learning, University at Buffalo	0.41 hours
Introduction to Machine Learning, University of Alberta	0.41 hours
Introduction to Machine Learning, University of Bristol	0.29 hours
Radial Basis Networks	0.6 Hours.
Learning from Data, University of Edinburgh	0.79 hours
Machine Learning, University at Buffalo	0.38 hours
Backpropagation algorithm/Learning rules	0.9 Hours.
Learning from Data, University of Edinburgh	1.52 hours
Machine Learning, University at Buffalo	1.18 hours
Introduction to Machine Learning, University of Alberta	0.83 hours
Machine Learning, Carnegie Mellon University	0.48 hours
Introduction to Machine Learning, University of Bristol	0.43 hours
Regularization	0.5 Hours.
Machine Learning, University at Buffalo	0.52 hours
Bayesian learning	0.3 Hours.
Machine Learning, University at Buffalo	0.27 hours
Mixture Density Networks	0.5 Hours.
Machine Learning, University at Buffalo	0.45 hours
Recurrent Neural Networks	0.1 Hours.
Introduction to Machine Learning, University of Alberta	0.09 hours
Regression	1.1 Hours.
Practical Machine Learning, University of California, Berkeley	1.91 hours
Machine Learning, University at Buffalo	1.68 hours
Introduction to Machine Learning, Brown University	1.59 hours
Machine Learning, Carnegie Mellon University	1.08 hours
Learning from Data, University of Edinburgh	0.89 hours
Introduction to Machine Learning, University of Alberta	0.71 hours
Introduction to Machine Learning, University of Bristol	0.64 hours
Machine learning, University of British Columbia	0.12 hours
General intro	0.2 Hours.
Practical Machine Learning, University of California, Berkeley	0.45 hours
Machine Learning, University at Buffalo	0.3 hours
Machine Learning, Carnegie Mellon University	0.25 hours
Learning from Data, University of Edinburgh	0.18 hours
Introduction to Machine Learning, University of Alberta	0.17 hours
Introduction to Machine Learning, University of Bristol	0.13 hours
Machine learning, University of British Columbia	0.06 hours
Least-squares estimation	0.4 Hours.
Practical Machine Learning, University of California, Berkeley	0.84 hours
Learning from Data, University of Edinburgh	0.47 hours
Machine Learning, University at Buffalo	0.41 hours
Introduction to Machine Learning, University of Alberta	0.39 hours
Introduction to Machine Learning, University of Bristol	0.38 hours
Machine Learning, Carnegie Mellon University	0.2 hours
Introduction to Machine Learning, Brown University	0.2 hours
Linear Parameters/Non-linear Basis Functions	0.2 Hours.
Introduction to Machine Learning, Brown University	0.64 hours
Machine Learning, University at Buffalo	0.25 hours
Learning from Data, University of Edinburgh	0.24 hours
Introduction to Machine Learning, University of Alberta	0.15 hours
Introduction to Machine Learning, University of Bristol	0.13 hours
Practical Machine Learning, University of California, Berkeley	0.13 hours
Machine learning, University of British Columbia	0.03 hours
Splines	0.3 Hours.
Practical Machine Learning, University of California, Berkeley	0.26 hours
Kernel Regression	0.1 Hours.
Machine Learning, Carnegie Mellon University	0.25 hours
Practical Machine Learning, University of California, Berkeley	0.06 hours
Machine learning, University of British Columbia	0.03 hours
Locally weighted regression	0.4 Hours.
Machine Learning, Carnegie Mellon University	0.38 hours
Lasso Regression	0.2 Hours.
Practical Machine Learning, University of California, Berkeley	0.17 hours
Bayesian estimation	0.7 Hours.
Introduction to Machine Learning, Brown University	0.75 hours
Machine Learning, University at Buffalo	0.72 hours
Gaussian Processes	1.4 Hours.
Introduction to Machine Learning, Brown University	1.5 hours
Machine Learning, University at Buffalo	1.43 hours
Introduction to Machine Learning, University of Alberta	1.33 hours
Parameter Estimation	4.5 Hours.
Introduction to Machine Learning, Brown University	9.57 hours
Machine Learning, University at Buffalo	8.74 hours
Machine learning, University of British Columbia	5.36 hours
Machine Learning, University of Cambridge	4.93 hours
Machine Learning, Carnegie Mellon University	3.68 hours
Learning from Data, University of Edinburgh	3.2 hours
Introduction to Machine Learning, University of Alberta	2.62 hours
Practical Machine Learning, University of California, Berkeley	2.21 hours
Introduction to Machine Learning, University of Bristol	0.13 hours
Maximum Likelihood	0.5 Hours.
Introduction to Machine Learning, Brown University	1.5 hours
Learning from Data, University of Edinburgh	1.07 hours
Machine Learning, Carnegie Mellon University	0.55 hours
Machine learning, University of British Columbia	0.46 hours
Machine Learning, University of Cambridge	0.36 hours
Introduction to Machine Learning, University of Alberta	0.16 hours
Practical Machine Learning, University of California, Berkeley	0.15 hours
Machine Learning, University at Buffalo	0.11 hours
Maximum a Posteriori / Bayesian	0.6 Hours.
Introduction to Machine Learning, Brown University	2.25 hours
Machine learning, University of British Columbia	0.92 hours
Machine Learning, Carnegie Mellon University	0.69 hours
Introduction to Machine Learning, University of Alberta	0.22 hours
Practical Machine Learning, University of California, Berkeley	0.18 hours
Learning from Data, University of Edinburgh	0.14 hours
Machine Learning, University at Buffalo	0.1 hours
Machine Learning, University of Cambridge	0.09 hours
Expectation Maximisation	0.7 Hours.
Machine Learning, Carnegie Mellon University	1.62 hours
Machine Learning, University at Buffalo	1.4 hours
Introduction to Machine Learning, Brown University	1.21 hours
Machine learning, University of British Columbia	0.5 hours
Machine Learning, University of Cambridge	0.48 hours
Learning from Data, University of Edinburgh	0.42 hours
Introduction to Machine Learning, University of Alberta	0.16 hours
Practical Machine Learning, University of California, Berkeley	0.13 hours
Sampling Methods	1.9 Hours.
Machine learning, University of British Columbia	3.0 hours
Machine Learning, University at Buffalo	2.83 hours
Machine Learning, University of Cambridge	2.0 hours
Introduction to Machine Learning, Brown University	1.5 hours
Introduction to Machine Learning, University of Alberta	0.12 hours
Variational Methods	1.9 Hours.
Machine Learning, University at Buffalo	2.83 hours
Machine Learning, University of Cambridge	1.0 hours
Evolutionary Methods	0.0 Hours.
Model Selection	1.0 Hours.
Introduction to Machine Learning, University of Alberta	1.96 hours
Practical Machine Learning, University of California, Berkeley	1.77 hours
Introduction to Machine Learning, Brown University	1.61 hours
Learning from Data, University of Edinburgh	0.95 hours
Machine Learning, Carnegie Mellon University	0.82 hours
Machine Learning, University at Buffalo	0.79 hours
Machine learning, University of British Columbia	0.48 hours
Introduction to Machine Learning, University of Bristol	0.13 hours
Machine Learning, University of Cambridge	0.11 hours
Cross-validation/Model Search	0.4 Hours.
Introduction to Machine Learning, Brown University	0.96 hours
Practical Machine Learning, University of California, Berkeley	0.81 hours
Introduction to Machine Learning, University of Alberta	0.36 hours
Machine Learning, University at Buffalo	0.1 hours
Machine learning, University of British Columbia	0.09 hours
Learning from Data, University of Edinburgh	0.07 hours
Overfitting, Train-vs-test error	0.3 Hours.
Machine Learning, Carnegie Mellon University	0.47 hours
Learning from Data, University of Edinburgh	0.38 hours
Machine learning, University of British Columbia	0.3 hours
Introduction to Machine Learning, Brown University	0.29 hours
Introduction to Machine Learning, University of Alberta	0.27 hours
Practical Machine Learning, University of California, Berkeley	0.21 hours
Machine Learning, University of Cambridge	0.11 hours
Machine Learning, University at Buffalo	0.03 hours
Bias-vs-variance Tradeoff	0.4 Hours.
Introduction to Machine Learning, University of Alberta	1.02 hours
Machine Learning, Carnegie Mellon University	0.35 hours
Machine Learning, University at Buffalo	0.34 hours
Learning from Data, University of Edinburgh	0.21 hours
Machine learning, University of British Columbia	0.09 hours
Regularization	0.3 Hours.
Practical Machine Learning, University of California, Berkeley	0.47 hours
Introduction to Machine Learning, Brown University	0.36 hours
Introduction to Machine Learning, University of Alberta	0.31 hours
Machine Learning, University at Buffalo	0.3 hours
Learning from Data, University of Edinburgh	0.29 hours
Model Complexity Criteria (eg. BIC)	0.1 Hours.
Introduction to Machine Learning, University of Bristol	0.13 hours
Machine Learning, University at Buffalo	0.02 hours
Bayesian Model Comparison	0.8 Hours.
Machine Learning, University of Cambridge	0.89 hours
Machine Learning, University at Buffalo	0.68 hours
Optimization (general)	0.5 Hours.
Introduction to Machine Learning, Brown University	0.75 hours
Learning from Data, University of Edinburgh	0.62 hours
Practical Machine Learning, University of California, Berkeley	0.12 hours
Maximum Entropy Estimation	0.8 Hours.
Introduction to Machine Learning, Brown University	0.75 hours
Maximum Mutual Information	0.0 Hours.
Density Estimation	0.9 Hours.
Learning from Data, University of Edinburgh	0.87 hours
k-Nearest Neighbours	0.0 Hours.
Vector Quantization-based density est	0.0 Hours.
Parzen windows/kernel density estimation	0.0 Hours.
Histogram-based density estimation	0.3 Hours.
Learning from Data, University of Edinburgh	0.25 hours
Gaussian Mixture Models	0.4 Hours.
Machine Learning, University at Buffalo	0.89 hours
Introduction to Machine Learning, Brown University	0.75 hours
Machine learning, University of British Columbia	0.5 hours
Learning from Data, University of Edinburgh	0.38 hours
Practical Machine Learning, University of California, Berkeley	0.3 hours
Machine Learning, Carnegie Mellon University	0.22 hours
Introduction to Machine Learning, University of Alberta	0.02 hours
Bayesian Networks/Graphical Models	2.8 Hours.
Machine learning, University of British Columbia	8.36 hours
Machine Learning, Carnegie Mellon University	4.06 hours
Machine Learning, University at Buffalo	2.98 hours
Introduction to Machine Learning, Brown University	2.5 hours
Introduction to Machine Learning, University of Bristol	2.22 hours
Machine Learning, University of Cambridge	2.01 hours
Introduction to Machine Learning, University of Alberta	1.7 hours
Learning from Data, University of Edinburgh	1.0 hours
Practical Machine Learning, University of California, Berkeley	0.1 hours
Belief Networks	0.8 Hours.
Machine Learning, Carnegie Mellon University	1.26 hours
Introduction to Machine Learning, Brown University	1.0 hours
Machine learning, University of British Columbia	1.0 hours
Machine Learning, University at Buffalo	0.83 hours
Introduction to Machine Learning, University of Bristol	0.73 hours
Machine Learning, University of Cambridge	0.57 hours
Introduction to Machine Learning, University of Alberta	0.15 hours
Conditional Independence	0.3 Hours.
Machine Learning, University at Buffalo	0.59 hours
Machine Learning, University of Cambridge	0.48 hours
Machine Learning, Carnegie Mellon University	0.2 hours
Introduction to Machine Learning, University of Alberta	0.15 hours
Introduction to Machine Learning, University of Bristol	0.07 hours
Practical Machine Learning, University of California, Berkeley	0.05 hours
Naïve Bayes	0.7 Hours.
Introduction to Machine Learning, Brown University	1.5 hours
Introduction to Machine Learning, University of Bristol	1.22 hours
Machine Learning, Carnegie Mellon University	1.04 hours
Learning from Data, University of Edinburgh	1.0 hours
Machine Learning, University at Buffalo	0.15 hours
Practical Machine Learning, University of California, Berkeley	0.05 hours
Introduction to Machine Learning, University of Alberta	0.02 hours
Markov Random Fields	1.4 Hours.
Machine learning, University of British Columbia	3.36 hours
Machine Learning, University at Buffalo	0.41 hours
Machine Learning, University of Cambridge	0.38 hours
Inference	0.7 Hours.
Machine Learning, Carnegie Mellon University	1.2 hours
Machine learning, University of British Columbia	1.0 hours
Machine Learning, University at Buffalo	1.0 hours
Machine Learning, University of Cambridge	0.48 hours
Introduction to Machine Learning, University of Alberta	0.22 hours
Introduction to Machine Learning, University of Bristol	0.13 hours
Applications	0.4 Hours.
Machine learning, University of British Columbia	1.0 hours
Machine Learning, Carnegie Mellon University	0.21 hours
Introduction to Machine Learning, University of Alberta	0.11 hours
Parameter Estimation	0.5 Hours.
Introduction to Machine Learning, University of Alberta	1.05 hours
Machine learning, University of British Columbia	1.0 hours
Machine Learning, Carnegie Mellon University	0.15 hours
Machine Learning, University of Cambridge	0.1 hours
Introduction to Machine Learning, University of Bristol	0.07 hours
Causality	1.0 Hours.
Machine learning, University of British Columbia	1.0 hours
Structure Learning	1.5 Hours.
Machine Learning, Carnegie Mellon University	1.6 hours
Introduction to Machine Learning, University of Alberta	1.33 hours
Classifier Combination	0.9 Hours.
Machine Learning, University at Buffalo	2.83 hours
Introduction to Machine Learning, University of Alberta	1.0 hours
Machine Learning, Carnegie Mellon University	0.76 hours
Learning from Data, University of Edinburgh	0.42 hours
Introduction to Machine Learning, University of Bristol	0.28 hours
Practical Machine Learning, University of California, Berkeley	0.05 hours
Bagging	0.1 Hours.
Introduction to Machine Learning, University of Alberta	0.23 hours
Introduction to Machine Learning, University of Bristol	0.14 hours
Practical Machine Learning, University of California, Berkeley	0.05 hours
Boosting/Adaboost	1.1 Hours.
Machine Learning, University at Buffalo	2.83 hours
Introduction to Machine Learning, University of Alberta	0.77 hours
Machine Learning, Carnegie Mellon University	0.76 hours
Introduction to Machine Learning, University of Bristol	0.14 hours
Other Ensemble Methods	0.2 Hours.
Introduction to Machine Learning, University of Alberta	0.32 hours
Practical Machine Learning, University of California, Berkeley	0.05 hours
Sequence Models	1.8 Hours.
Introduction to Machine Learning, Brown University	2.54 hours
Machine learning, University of British Columbia	2.0 hours
Practical Machine Learning, University of California, Berkeley	2.0 hours
Machine Learning, University at Buffalo	1.84 hours
Introduction to Machine Learning, University of Alberta	1.33 hours
Machine Learning, Carnegie Mellon University	1.22 hours
Markov Models	0.4 Hours.
Machine learning, University of British Columbia	1.0 hours
Machine Learning, University at Buffalo	0.36 hours
Practical Machine Learning, University of California, Berkeley	0.26 hours
Introduction to Machine Learning, Brown University	0.23 hours
Introduction to Machine Learning, University of Alberta	0.23 hours
Hidden Markov Models	1.3 Hours.
Introduction to Machine Learning, Brown University	2.31 hours
Machine Learning, Carnegie Mellon University	1.22 hours
Practical Machine Learning, University of California, Berkeley	1.15 hours
Machine Learning, University at Buffalo	1.13 hours
Machine learning, University of British Columbia	1.0 hours
Introduction to Machine Learning, University of Alberta	1.0 hours
Linear Dynamical Systems	0.4 Hours.
Machine Learning, University at Buffalo	0.7 hours
Practical Machine Learning, University of California, Berkeley	0.33 hours
Introduction to Machine Learning, University of Alberta	0.05 hours
Kalman Filters	0.2 Hours.
Practical Machine Learning, University of California, Berkeley	0.33 hours
Introduction to Machine Learning, University of Alberta	0.05 hours
Particle filters	0.2 Hours.
Practical Machine Learning, University of California, Berkeley	0.26 hours
Introduction to Machine Learning, University of Alberta	0.05 hours
Conditional Random Fields	1.0 Hours.
Machine Learning, University at Buffalo	0.99 hours
Applications	1.2 Hours.
Practical Machine Learning, University of California, Berkeley	2.15 hours
Introduction to Machine Learning, University of Bristol	2.0 hours
Machine Learning, Carnegie Mellon University	0.25 hours
Machine Learning, University at Buffalo	0.23 hours
Spam Email Filtering	1.0 Hours.
Introduction to Machine Learning, University of Bristol	1.0 hours
Collaborative filtering	1.5 Hours.
Practical Machine Learning, University of California, Berkeley	2.0 hours
Introduction to Machine Learning, University of Bristol	1.0 hours
Face recognition	0.0 Hours.
Hand-writing recognition	0.0 Hours.
Color-Based Skin Detection in Images	0.0 Hours.
Face detection	0.0 Hours.
Robot Juggling	0.0 Hours.
Document image analysis	0.0 Hours.
Fingerprint identification	0.0 Hours.
Object Tracking	0.0 Hours.
Brain MR Image Segmentation	0.0 Hours.
Human Pose Estimation	0.0 Hours.
Speech recognition	0.0 Hours.
Visual analysis of art	0.0 Hours.
Music information retrieval	0.0 Hours.
Text classification	0.2 Hours.
Machine Learning, Carnegie Mellon University	0.25 hours
Machine Learning, University at Buffalo	0.23 hours
Practical Machine Learning, University of California, Berkeley	0.09 hours
Network anomaly detection	0.1 Hours.
Practical Machine Learning, University of California, Berkeley	0.06 hours
Brain Imaging fmri/eeg interpretation	0.5 Hours.
Introduction to Machine Learning, University of Bristol	1.0 hours
Machine Learning, Carnegie Mellon University	0.27 hours
Practical Machine Learning, University of California, Berkeley	0.09 hours
Theoretical concepts	2.3 Hours.
Machine Learning, Carnegie Mellon University	3.55 hours
Machine Learning, University at Buffalo	2.84 hours
Introduction to Machine Learning, University of Bristol	2.44 hours
Machine learning, University of British Columbia	1.79 hours
Introduction to Machine Learning, University of Alberta	0.69 hours
No free lunch theorem	0.0 Hours.
Machine learning, University of British Columbia	0.03 hours
VC Dimension/inequality	0.7 Hours.
Machine Learning, University at Buffalo	1.4 hours
Machine Learning, Carnegie Mellon University	0.83 hours
Machine learning, University of British Columbia	0.65 hours
Introduction to Machine Learning, University of Alberta	0.27 hours
Introduction to Machine Learning, University of Bristol	0.22 hours
PAC Learning	0.9 Hours.
Machine Learning, Carnegie Mellon University	1.39 hours
Machine Learning, University at Buffalo	0.97 hours
Machine learning, University of British Columbia	0.63 hours
Introduction to Machine Learning, University of Alberta	0.42 hours
Mistake Bound	0.5 Hours.
Machine Learning, University at Buffalo	0.47 hours
Computational Learning Theory	1.3 Hours.
Introduction to Machine Learning, University of Bristol	2.22 hours
Machine Learning, Carnegie Mellon University	1.33 hours
Machine learning, University of British Columbia	0.48 hours
Bayesian Concept Learning	3.0 Hours.
Machine learning, University of British Columbia	3.0 hours
Miscellaneous other areas	2.0 Hours.
Practical Machine Learning, University of California, Berkeley	8.0 hours
Machine Learning, University of Cambridge	3.0 hours
Machine Learning, Carnegie Mellon University	2.21 hours
Introduction to Machine Learning, University of Alberta	1.33 hours
Learning from Data, University of Edinburgh	1.0 hours
Machine Learning, University at Buffalo	0.49 hours
Introduction to Machine Learning, University of Bristol	0.13 hours
Machine learning, University of British Columbia	0.06 hours
Data Visualisation	1.8 Hours.
Learning from Data, University of Edinburgh	2.0 hours
Practical Machine Learning, University of California, Berkeley	1.68 hours
Multidimensional scaling	0.2 Hours.
Practical Machine Learning, University of California, Berkeley	0.23 hours
Isomap	0.4 Hours.
Practical Machine Learning, University of California, Berkeley	0.39 hours
Local Linear Embedding	0.4 Hours.
Practical Machine Learning, University of California, Berkeley	0.42 hours
Laplacian Eigenmaps	0.3 Hours.
Practical Machine Learning, University of California, Berkeley	0.29 hours
Maximum variance Unfolding	0.4 Hours.
Practical Machine Learning, University of California, Berkeley	0.35 hours
Conformal Components Analysis	0.3 Hours.
Practical Machine Learning, University of California, Berkeley	0.32 hours
Novelty/outlier detection	1.0 Hours.
Practical Machine Learning, University of California, Berkeley	2.0 hours
Machine learning, University of British Columbia	0.03 hours
Reinforcement Learning	1.5 Hours.
Machine Learning, University of Cambridge	3.0 hours
Machine Learning, Carnegie Mellon University	2.21 hours
Practical Machine Learning, University of California, Berkeley	2.0 hours
Introduction to Machine Learning, University of Alberta	1.33 hours
Introduction to Machine Learning, University of Bristol	0.13 hours
Machine learning, University of British Columbia	0.03 hours
Active Learning	2.0 Hours.
Practical Machine Learning, University of California, Berkeley	2.0 hours
Semi-supervised Learning	0.0 Hours.
MATLAB Programming	0.5 Hours.
Machine Learning, University at Buffalo	0.49 hours