| Lecture 1 View Now > |
1 hr 9 min
- Topics: The Motivation & Applications of Machine Learning, The Logistics of the Class, The Definition of Machine Learning, The Overview of Supervised Learning, The Overview of Learning Theory, The Overview of Unsupervised Learning, The Overview of Reinforcement Learning
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| Lecture 2 View Now > |
1 hr 16 min
- Topics: An Application of Supervised Learning – Autonomous Deriving, ALVINN, Linear Regression, Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent (Incremental Descent), Matrix Derivative Notation for Deriving Normal Equations, Derivation of Normal Equations
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| Lecture 3 View Now > |
1 hr 13 min
- Topics: The Concept of Underfitting and Overfitting, The Concept of Parametric Algorithms and Non-parametric Algorithms, Locally Weighted Regression, The Probabilistic Interpretation of Linear Regression, The motivation of Logistic Regression, Logistic Regression, Perceptron
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| Lecture 4 View Now > |
1 hr 13 min
- Topics: Newton’s Method, Exponential Family, Bernoulli Example, Gaussian Example, General Linear Models (GLMs), Multinomial Example, Softmax Regression
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| Lecture 5 View Now > |
1 hr 16 min
- Topics: Discriminative Algorithms, Generative Algorithms, Gaussian Discriminant Analysis (GDA), GDA and Logistic Regression, Naive Bayes, Laplace Smoothing
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| Lecture 6 View Now > |
1 hr 14 min
- Topics: Multinomial Event Model, Non-linear Classifiers, Neural Network, Applications of Neural Network, Intuitions about Support Vector Machine (SVM), Notation for SVM, Functional and Geometric Margins
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| Lecture 7 View Now > |
1 hr 16 min
- Topics: Optimal Margin Classifier, Lagrange Duality, Karush-Kuhn-Tucker (KKT) Conditions, SVM Dual, The Concept of Kernels
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| Lecture 8 View Now > |
1 hr 17 min
- Topics: Kernels, Mercer’s Theorem, Non-linear Decision Boundaries and Soft Margin SVM, Coordinate Ascent Algorithm, The Sequential Minimization Optimization (SMO) Algorithm, Applications of SVM
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| Lecture 9 View Now > |
1 hr 14 min
- Topics: Bias/variance Tradeoff, Empirical Risk Minimization (ERM), The Union Bound, Hoeffding Inequality, Uniform Convergence – The Case of Finite H, Sample Complexity Bound, Error Bound, Uniform Convergence Theorem & Corollary
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| Lecture 10 View Now > |
1 hr 13 min
- Topics: Uniform Convergence – The Case of Infinite H, The Concept of ‘Shatter’ and VC Dimension, SVM Example, Model Selection, Cross Validation, Feature Selection
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| Lecture 11 View Now > |
1 hr 22 min
- Topics: Bayesian Statistics and Regularization, Online Learning, Advice for Applying Machine Learning Algorithms, Debugging/fixing Learning Algorithms, Diagnostics for Bias & Variance, Optimization Algorithm Diagnostics, Diagnostic Example – Autonomous Helicopter, Error Analysis, Getting Started on a Learning Problem
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| Lecture 12 View Now > |
1 hr 14 min
- Topics: The Concept of Unsupervised Learning, K-means Clustering Algorithm, K-means Algorithm, Mixtures of Gaussians and the EM Algorithm, Jensen’s Inequality, The EM Algorithm, Summary
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| Lecture 13 View Now > |
1 hr 15 min
- Topics: Mixture of Gaussian, Mixture of Naive Bayes – Text clustering (EM Application), Factor Analysis, Restrictions on a Covariance Matrix, The Factor Analysis Model, EM for Factor Analysis
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| Lecture 14 View Now > |
1 hr 21 min
- Topics: The Factor Analysis Model,0 EM for Factor Analysis, Principal Component Analysis (PCA), PCA as a Dimensionality Reduction Algorithm, Applications of PCA, Face Recognition by Using PCA
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| Lecture 15 View Now > |
1 hr 17 min
- Topics: Latent Semantic Indexing (LSI), Singular Value Decomposition (SVD) Implementation, Independent Component Analysis (ICA), The Application of ICA, Cumulative Distribution Function (CDF), ICA Algorithm, The Applications of ICA
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| Lecture 16 View Now > |
1 hr 13 min
- Topics: Applications of Reinforcement Learning, Markov Decision Process (MDP), Defining Value & Policy Functions, Value Function, Optimal Value Function, Value Iteration, Policy Iteration
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| Lecture 17 View Now > |
1 hr 17 min
- Topics: Generalization to Continuous States, Discretization & Curse of Dimensionality, Models/Simulators, Fitted Value Iteration, Finding Optimal Policy
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| Lecture 18 View Now > |
1 hr 17 min
- Topics: State-action Rewards, Finite Horizon MDPs, The Concept of Dynamical Systems, Examples of Dynamical Models, Linear Quadratic Regulation (LQR), Linearizing a Non-Linear Model, Computing Rewards, Riccati Equation
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| Lecture 19 View Now > |
1 hr 16 min
- Topics: Advice for Applying Machine Learning, Debugging Reinforcement Learning (RL) Algorithm, Linear Quadratic Regularization (LQR), Differential Dynamic Programming (DDP), Kalman Filter & Linear Quadratic Gaussian (LQG), Predict/update Steps of Kalman Filter, Linear Quadratic Gaussian (LQG)
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| Lecture 20 View Now > |
1 hr 17 min
- Topics: Partially Observable MDPs (POMDPs), Policy Search, Reinforce Algorithm, Pegasus Algorithm, Pegasus Policy Search, Applications of Reinforcement Learning
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