Machine Learning
De Stanford
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Descripción del curso
This course provides a broad introduction to machine learning and statistical pattern recognition.
Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Students are expected to have the following background:
- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
- Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)
- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)
This Stanford course was taught on campus twice per week in 75 minute lectures for the Stanford Engineering Everywhere Initiative.
Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
Students are expected to have the following background:
- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
- Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)
- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)
This Stanford course was taught on campus twice per week in 75 minute lectures for the Stanford Engineering Everywhere Initiative.
| Título | Descripción | Duración | Precio | ||
|---|---|---|---|---|---|
| 1 | 1. Machine Learning Lecture 1 | science, math, engineering, computer, technology, robotics, reinforcement, supervised, learning, algorithm, machine, image processing, ICA, theory, programming, code | 1:08:39 | Gratis | Ver en iTunes |
| 2 | Linear Algebra Review and Reference | -- | -- | Gratis | Ver en iTunes |
| 3 | Probability Theory Review | -- | -- | Gratis | Ver en iTunes |
| 4 | Convex Optimization Overview Part I | -- | -- | Gratis | Ver en iTunes |
| 5 | Convex Optimization Overview Part II | -- | -- | Gratis | Ver en iTunes |
| 6 | Hidden Markov Models | -- | -- | Gratis | Ver en iTunes |
| 7 | Gaussian Processes | -- | -- | Gratis | Ver en iTunes |
| 8 | 2. Machine Learning Lecture 2 | science, math, engineering, computer, technology, robotics, algebra, linear regression, learning, algorithm, regression, gradient descent, normal equation | 1:16:15 | Gratis | Ver en iTunes |
| 9 | Linear Regression, Classification and Logistic Regression, Generalized Linear Models | -- | -- | Gratis | Ver en iTunes |
| 10 | 3. Machine Learning Lecture 3 | science, math, engineering, computer, technology, robotics, algebra, locally, weighted, logistic, regression, linear, probabilistic, interpretation, Gaussian, distribution, digression, perceptron | 1:13:13 | Gratis | Ver en iTunes |
| 11 | Problem Set 1 | -- | -- | Gratis | Ver en iTunes |
| 12 | 4. Machine Learning Lecture 4 | science, math, engineering, computer, technology, robotics, logistic, regression, Newton, method, exponential, family, generalized, linear, model, multinomial, softmax | 1:13:06 | Gratis | Ver en iTunes |
| 13 | 5. Machine Learning Lecture 5 | science, math, engineering, computer, technology, robotics, learning, algorithm, generative, Gaussian, discriminative, analysis, digression, naive, Bayes, Laplace smoothing | 1:15:30 | Gratis | Ver en iTunes |
| 14 | Generative Learning Algorithms | -- | -- | Gratis | Ver en iTunes |
| 15 | 6. Machine Learning Lecture 6 | science, math, engineering, computer, technology, robotics, learning, algorithm, naive, Bayes, multinomial, multivariate, Bernoulli, event, model, neural, networks, support, vector, machines | 1:13:08 | Gratis | Ver en iTunes |
| 16 | 7. Machine Learning Lecture 7 | science, math, engineering, computer, technology, robotics, learning, algorithm, optimal, margin, classifier, prime, dual, optimization, SVM, dual, kernels, convex, KKT | 1:15:44 | Gratis | Ver en iTunes |
| 17 | Support Vector Machines | -- | -- | Gratis | Ver en iTunes |
| 18 | Problem Set 1 Solutions | -- | -- | Gratis | Ver en iTunes |
| 19 | 8. Machine Learning Lecture 8 | science, math, engineering, computer, technology, robotics, learning, algorithm, support, vector, machine, SVM, kernel, soft margin, optimization, SMO | 1:17:18 | Gratis | Ver en iTunes |
| 20 | Learning Theory | -- | -- | Gratis | Ver en iTunes |
| 21 | Problem Set 2 | -- | -- | Gratis | Ver en iTunes |
| 22 | 9. Machine Learning Lecture 9 | science, math, engineering, computer, technology, robotics, learning, algorithm, theory, bias, variance, empirical risk minimization, ERM, union bound, Boole, Hoeffding, inequality, uniform convergence | 1:14:18 | Gratis | Ver en iTunes |
| 23 | 10. Machine Learning Lecture 10 | science, math, engineering, computer, technology, robotics, learning, algorithm, theory, VC, dimension, model, selection, hypothesis, class, floating, point, number, Vapnik, Chervonenkis | 1:12:55 | Gratis | Ver en iTunes |
| 24 | Regularization and Model Selection | -- | -- | Gratis | Ver en iTunes |
| 25 | Midterm Exam | -- | -- | Gratis | Ver en iTunes |
| 26 | 11. Machine Learning Lecture 11 | science, math, engineering, computer, technology, robotics, learning, algorithm, theory, Bayesian, statistics, regularization, digression, online, machine, ML | 1:22:18 | Gratis | Ver en iTunes |
| 27 | The Perceptron and Large Margin Classifiers | -- | -- | Gratis | Ver en iTunes |
| 28 | Problem Set 2 Solutions | -- | -- | Gratis | Ver en iTunes |
| 29 | 12. Machine Learning Lecture 12 | science, math, engineering, computer, technology, robotics, learning, algorithm, unsupervised, clustering, k-means, mixture, gaussians, Jensen's inequality, expectation, maximization, EM | 1:14:22 | Gratis | Ver en iTunes |
| 30 | K-Means Clustering Algorithm | -- | -- | Gratis | Ver en iTunes |
| 31 | Mixtures of Gaussians and the EM Algorithm | -- | -- | Gratis | Ver en iTunes |
| 32 | Problem Set 3 | -- | -- | Gratis | Ver en iTunes |
| 33 | 13. Machine Learning Lecture 13 | science, math, engineering, computer, technology, robotics, learning, algorithm, expectation, maximization, EM, mixture, Gaussian, naive Bayes, model, factor analysis, digression, distribution, unsupervised | 1:14:56 | Gratis | Ver en iTunes |
| 34 | The EM Algorithm | -- | -- | Gratis | Ver en iTunes |
| 35 | Factor Analysis | -- | -- | Gratis | Ver en iTunes |
| 36 | 14. Machine Learning Lecture 14 | science, math, engineering, computer, technology, robotics, learning, algorithm, unsupervised, expectation, maximization, EM, principal, component, analysis, PCA | 1:20:39 | Gratis | Ver en iTunes |
| 37 | Principal Components Analysis | -- | -- | Gratis | Ver en iTunes |
| 38 | 15. Machine Learning Lecture 15 | science, math, engineering, computer, technology, robotics, learning, algorithm, principal, independent, component, analysis, ICA, PCA, latent, semantic, indexing, LSI singular, value, decomposition, | 1:17:17 | Gratis | Ver en iTunes |
| 39 | Independent Components Analysis | -- | -- | Gratis | Ver en iTunes |
| 40 | Problem Set 3 Solutions | -- | -- | Gratis | Ver en iTunes |
| 41 | 16. Machine Learning Lecture 16 | science, math, engineering, computer, technology, robotics, reinforcement, learning, algorithm, MDP, Markov, decision, process, policy, value, function, iteration, bellman equation | 1:13:05 | Gratis | Ver en iTunes |
| 42 | Reinforcement Learning and Control | -- | -- | Gratis | Ver en iTunes |
| 43 | Problem Set 4 | -- | -- | Gratis | Ver en iTunes |
| 44 | 17. Machine Learning Lecture 17 | science, math, engineering, computer, technology, robotics, reinforcement, learning, algorithm, continuous, state, MDP, discretization, fitted value iteration, Q function, approximate policy, Markov, decision, process | 1:16:59 | Gratis | Ver en iTunes |
| 45 | 18. Machine Learning Lecture 18 | science, math, engineering, computer, technology, robotics, learning, algorithm, finite, horizon, state-action reward, MDP, linear, dynamical, system, quadratic, regulation, LQR, Riccati equation | 1:16:37 | Gratis | Ver en iTunes |
| 46 | 19. Machine Learning Lecture 19 | science, math, engineering, computer, technology, robotics, reinforcement, learning, algorithm, debugging, linear, quadratic, regulation, LQR, differential, dynamic, programming, DDP, Kalmer filter, Gaussian, LQG | 1:15:54 | Gratis | Ver en iTunes |
| 47 | Problem Set 4 Solutions | -- | -- | Gratis | Ver en iTunes |
| 48 | 20. Machine Learning Lecture 20 | science, math, engineering, computer, technology, robotics, reinforcement, learning, algorithm, POMDP, partially, observable, Markov, decision, process, policy, search, pegasus | 1:16:39 | Gratis | Ver en iTunes |
| Total: 48 episodios |
