[Coursera] Machine Learning by Andrew Ng

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[Coursera] Machine Learning by Andrew Ng

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收录时间：2017-02-10
最近下载：2025-06-01

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文件列表

  • 01. Introduction (Week 1)/1 - 1 - Welcome (7 min).mp4 12.5 MB
  • 01. Introduction (Week 1)/1 - 2 - What is Machine Learning- (7 min).mp4 9.8 MB
  • 01. Introduction (Week 1)/1 - 3 - Supervised Learning (12 min).mp4 14.1 MB
  • 01. Introduction (Week 1)/1 - 4 - Unsupervised Learning (14 min).mp4 17.5 MB
  • 01. Introduction (Week 1)/docs-slides-Lecture1.pdf 3.5 MB
  • 01. Introduction (Week 1)/docs-slides-Lecture1.pptx 4.2 MB
  • 02. Linear Regression with One Variable (Week 1)/2 - 1 - Model Representation (8 min).mp4 9.4 MB
  • 02. Linear Regression with One Variable (Week 1)/2 - 2 - Cost Function (8 min).mp4 9.5 MB
  • 02. Linear Regression with One Variable (Week 1)/2 - 3 - Cost Function - Intuition I (11 min).mp4 12.8 MB
  • 02. Linear Regression with One Variable (Week 1)/2 - 4 - Cost Function - Intuition II (9 min).mp4 11.9 MB
  • 02. Linear Regression with One Variable (Week 1)/2 - 5 - Gradient Descent (11 min).mp4 14.2 MB
  • 02. Linear Regression with One Variable (Week 1)/2 - 6 - Gradient Descent Intuition (12 min).mp4 13.7 MB
  • 02. Linear Regression with One Variable (Week 1)/2 - 7 - Gradient Descent For Linear Regression (10 min).mp4 12.8 MB
  • 02. Linear Regression with One Variable (Week 1)/2 - 8 - What-'s Next (6 min).mp4 6.4 MB
  • 02. Linear Regression with One Variable (Week 1)/docs-slides-Lecture2.pdf 3.0 MB
  • 02. Linear Regression with One Variable (Week 1)/docs-slides-Lecture2.pptx 5.6 MB
  • 03. Linear Algebra Review (Week 1, Optional)/3 - 1 - Matrices and Vectors (9 min).mp4 10.0 MB
  • 03. Linear Algebra Review (Week 1, Optional)/3 - 2 - Addition and Scalar Multiplication (7 min).mp4 7.8 MB
  • 03. Linear Algebra Review (Week 1, Optional)/3 - 3 - Matrix Vector Multiplication (14 min).mp4 15.7 MB
  • 03. Linear Algebra Review (Week 1, Optional)/3 - 4 - Matrix Matrix Multiplication (11 min).mp4 13.2 MB
  • 03. Linear Algebra Review (Week 1, Optional)/3 - 5 - Matrix Multiplication Properties (9 min).mp4 10.3 MB
  • 03. Linear Algebra Review (Week 1, Optional)/3 - 6 - Inverse and Transpose (11 min).mp4 13.5 MB
  • 03. Linear Algebra Review (Week 1, Optional)/docs-slides-Lecture3.pdf 1.9 MB
  • 03. Linear Algebra Review (Week 1, Optional)/docs-slides-Lecture3.pptx 5.2 MB
  • 04. Linear Regression with Multiple Variables (Week 2)/4 - 1 - Multiple Features (8 min).mp4 9.3 MB
  • 04. Linear Regression with Multiple Variables (Week 2)/4 - 2 - Gradient Descent for Multiple Variables (5 min).mp4 6.1 MB
  • 04. Linear Regression with Multiple Variables (Week 2)/4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp4 9.9 MB
  • 04. Linear Regression with Multiple Variables (Week 2)/4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp4 9.7 MB
  • 04. Linear Regression with Multiple Variables (Week 2)/4 - 5 - Features and Polynomial Regression (8 min).mp4 8.7 MB
  • 04. Linear Regression with Multiple Variables (Week 2)/4 - 6 - Normal Equation (16 min).mp4 18.0 MB
  • 04. Linear Regression with Multiple Variables (Week 2)/4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp4 6.5 MB
  • 04. Linear Regression with Multiple Variables (Week 2)/docs-slides-Lecture4.pdf 1.8 MB
  • 04. Linear Regression with Multiple Variables (Week 2)/docs-slides-Lecture4.pptx 4.6 MB
  • 05. Octave Tutorial (Week 2)/5 - 1 - Basic Operations (14 min).mp4 18.6 MB
  • 05. Octave Tutorial (Week 2)/5 - 2 - Moving Data Around (16 min).mp4 21.8 MB
  • 05. Octave Tutorial (Week 2)/5 - 3 - Computing on Data (13 min).mp4 16.0 MB
  • 05. Octave Tutorial (Week 2)/5 - 4 - Plotting Data (10 min).mp4 14.0 MB
  • 05. Octave Tutorial (Week 2)/5 - 5 - Control Statements- for, while, if statements (13 min).mp4 17.3 MB
  • 05. Octave Tutorial (Week 2)/5 - 6 - Vectorization (14 min).mp4 16.9 MB
  • 05. Octave Tutorial (Week 2)/5 - 7 - Working on and Submitting Programming Exercises (4 min).mp4 5.7 MB
  • 05. Octave Tutorial (Week 2)/docs-slides-Lecture5.pdf 248.2 kB
  • 05. Octave Tutorial (Week 2)/docs-slides-Lecture5.pptx 417.1 kB
  • 06. Logistic Regression (Week 3)/6 - 1 - Classification (8 min).mp4 9.2 MB
  • 06. Logistic Regression (Week 3)/6 - 2 - Hypothesis Representation (7 min).mp4 8.7 MB
  • 06. Logistic Regression (Week 3)/6 - 3 - Decision Boundary (15 min).mp4 17.6 MB
  • 06. Logistic Regression (Week 3)/6 - 4 - Cost Function (11 min).mp4 13.7 MB
  • 06. Logistic Regression (Week 3)/6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp4 12.5 MB
  • 06. Logistic Regression (Week 3)/6 - 6 - Advanced Optimization (14 min).mp4 19.0 MB
  • 06. Logistic Regression (Week 3)/6 - 7 - Multiclass Classification- One-vs-all (6 min).mp4 7.3 MB
  • 06. Logistic Regression (Week 3)/docs-slides-Lecture6.pdf 2.2 MB
  • 06. Logistic Regression (Week 3)/docs-slides-Lecture6.pptx 4.0 MB
  • 07. Regularization (Week 3)/7 - 1 - The Problem of Overfitting (10 min).mp4 11.7 MB
  • 07. Regularization (Week 3)/7 - 2 - Cost Function (10 min).mp4 12.2 MB
  • 07. Regularization (Week 3)/7 - 3 - Regularized Linear Regression (11 min).mp4 12.6 MB
  • 07. Regularization (Week 3)/7 - 4 - Regularized Logistic Regression (9 min).mp4 11.4 MB
  • 07. Regularization (Week 3)/docs-slides-Lecture7.pdf 2.5 MB
  • 07. Regularization (Week 3)/docs-slides-Lecture7.pptx 2.7 MB
  • 08. Neural Networks Representation (Week 4)/8 - 1 - Non-linear Hypotheses (10 min).mp4 11.4 MB
  • 08. Neural Networks Representation (Week 4)/8 - 2 - Neurons and the Brain (8 min).mp4 10.4 MB
  • 08. Neural Networks Representation (Week 4)/8 - 3 - Model Representation I (12 min).mp4 14.2 MB
  • 08. Neural Networks Representation (Week 4)/8 - 4 - Model Representation II (12 min).mp4 14.1 MB
  • 08. Neural Networks Representation (Week 4)/8 - 5 - Examples and Intuitions I (7 min).mp4 8.3 MB
  • 08. Neural Networks Representation (Week 4)/8 - 6 - Examples and Intuitions II (10 min).mp4 14.7 MB
  • 08. Neural Networks Representation (Week 4)/8 - 7 - Multiclass Classification (4 min).mp4 5.1 MB
  • 08. Neural Networks Representation (Week 4)/docs-slides-Lecture8.pdf 5.2 MB
  • 08. Neural Networks Representation (Week 4)/docs-slides-Lecture8.pptx 42.3 MB
  • 09. Neural Networks Learning (Week 5)/9 - 1 - Cost Function (7 min).mp4 8.0 MB
  • 09. Neural Networks Learning (Week 5)/9 - 2 - Backpropagation Algorithm (12 min).mp4 14.6 MB
  • 09. Neural Networks Learning (Week 5)/9 - 3 - Backpropagation Intuition (13 min).mp4 16.2 MB
  • 09. Neural Networks Learning (Week 5)/9 - 4 - Implementation Note- Unrolling Parameters (8 min).mp4 9.8 MB
  • 09. Neural Networks Learning (Week 5)/9 - 5 - Gradient Checking (12 min).mp4 14.2 MB
  • 09. Neural Networks Learning (Week 5)/9 - 6 - Random Initialization (7 min).mp4 7.9 MB
  • 09. Neural Networks Learning (Week 5)/9 - 7 - Putting It Together (14 min).mp4 17.1 MB
  • 09. Neural Networks Learning (Week 5)/9 - 8 - Autonomous Driving (7 min).mp4 15.6 MB
  • 09. Neural Networks Learning (Week 5)/docs-slides-Lecture9.pdf 3.5 MB
  • 09. Neural Networks Learning (Week 5)/docs-slides-Lecture9.pptx 5.2 MB
  • 10. Advice for Applying Machine Learning (Week 6)/10 - 1 - Deciding What to Try Next (6 min).mp4 7.2 MB
  • 10. Advice for Applying Machine Learning (Week 6)/10 - 2 - Evaluating a Hypothesis (8 min).mp4 8.9 MB
  • 10. Advice for Applying Machine Learning (Week 6)/10 - 3 - Model Selection and Train-Validation-Test Sets (12 min).mp4 14.8 MB
  • 10. Advice for Applying Machine Learning (Week 6)/10 - 4 - Diagnosing Bias vs. Variance (8 min).mp4 9.4 MB
  • 10. Advice for Applying Machine Learning (Week 6)/10 - 5 - Regularization and Bias-Variance (11 min).mp4 13.2 MB
  • 10. Advice for Applying Machine Learning (Week 6)/10 - 6 - Learning Curves (12 min).mp4 13.5 MB
  • 10. Advice for Applying Machine Learning (Week 6)/10 - 7 - Deciding What to Do Next Revisited (7 min).mp4 8.6 MB
  • 10. Advice for Applying Machine Learning (Week 6)/docs-slides-Lecture10.pdf 1.6 MB
  • 10. Advice for Applying Machine Learning (Week 6)/docs-slides-Lecture10.pptx 3.5 MB
  • 11. Machine Learning System Design (Week 6)/11 - 1 - Prioritizing What to Work On (10 min).mp4 11.7 MB
  • 11. Machine Learning System Design (Week 6)/11 - 2 - Error Analysis (13 min).mp4 16.2 MB
  • 11. Machine Learning System Design (Week 6)/11 - 3 - Error Metrics for Skewed Classes (12 min).mp4 13.9 MB
  • 11. Machine Learning System Design (Week 6)/11 - 4 - Trading Off Precision and Recall (14 min).mp4 16.8 MB
  • 11. Machine Learning System Design (Week 6)/11 - 5 - Data For Machine Learning (11 min).mp4 13.5 MB
  • 11. Machine Learning System Design (Week 6)/docs-slides-Lecture11.pdf 509.6 kB
  • 11. Machine Learning System Design (Week 6)/docs-slides-Lecture11.pptx 2.0 MB
  • 12. Support Vector Machines (Week 7)/12 - 1 - Optimization Objective (15 min).mp4 17.5 MB
  • 12. Support Vector Machines (Week 7)/12 - 2 - Large Margin Intuition (11 min).mp4 12.4 MB
  • 12. Support Vector Machines (Week 7)/12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4 22.9 MB
  • 12. Support Vector Machines (Week 7)/12 - 4 - Kernels I (16 min).mp4 18.4 MB
  • 12. Support Vector Machines (Week 7)/12 - 5 - Kernels II (16 min).mp4 18.3 MB
  • 12. Support Vector Machines (Week 7)/12 - 6 - Using An SVM (21 min).mp4 25.1 MB
  • 12. Support Vector Machines (Week 7)/docs-slides-Lecture12.pdf 2.4 MB
  • 12. Support Vector Machines (Week 7)/docs-slides-Lecture12.pptx 5.6 MB
  • 13. Clustering (Week 8)/13 - 1 - Unsupervised Learning- Introduction (3 min).mp4 4.0 MB
  • 13. Clustering (Week 8)/13 - 2 - K-Means Algorithm (13 min).mp4 14.5 MB
  • 13. Clustering (Week 8)/13 - 3 - Optimization Objective (7 min).mp4 8.5 MB
  • 13. Clustering (Week 8)/13 - 4 - Random Initialization (8 min).mp4 9.1 MB
  • 13. Clustering (Week 8)/13 - 5 - Choosing the Number of Clusters (8 min).mp4 9.9 MB
  • 13. Clustering (Week 8)/docs-slides-Lecture13.pdf 2.3 MB
  • 13. Clustering (Week 8)/docs-slides-Lecture13.pptx 2.9 MB
  • 14. Dimensionality Reduction (Week 8)/14 - 1 - Motivation I- Data Compression (10 min).mp4 15.0 MB
  • 14. Dimensionality Reduction (Week 8)/14 - 2 - Motivation II- Visualization (6 min).mp4 6.6 MB
  • 14. Dimensionality Reduction (Week 8)/14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp4 11.0 MB
  • 14. Dimensionality Reduction (Week 8)/14 - 4 - Principal Component Analysis Algorithm (15 min).mp4 18.7 MB
  • 14. Dimensionality Reduction (Week 8)/14 - 5 - Choosing the Number of Principal Components (11 min).mp4 12.4 MB
  • 14. Dimensionality Reduction (Week 8)/14 - 6 - Reconstruction from Compressed Representation (4 min).mp4 5.2 MB
  • 14. Dimensionality Reduction (Week 8)/14 - 7 - Advice for Applying PCA (13 min).mp4 15.4 MB
  • 14. Dimensionality Reduction (Week 8)/docs-slides-Lecture14.pdf 1.7 MB
  • 14. Dimensionality Reduction (Week 8)/docs-slides-Lecture14.pptx 3.8 MB
  • 15. Anomaly Detection (Week 9)/15 - 1 - Problem Motivation (8 min).mp4 8.8 MB
  • 15. Anomaly Detection (Week 9)/15 - 2 - Gaussian Distribution (10 min).mp4 12.3 MB
  • 15. Anomaly Detection (Week 9)/15 - 3 - Algorithm (12 min).mp4 14.6 MB
  • 15. Anomaly Detection (Week 9)/15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4 15.9 MB
  • 15. Anomaly Detection (Week 9)/15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp4 9.7 MB
  • 15. Anomaly Detection (Week 9)/15 - 6 - Choosing What Features to Use (12 min).mp4 14.8 MB
  • 15. Anomaly Detection (Week 9)/15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4 16.7 MB
  • 15. Anomaly Detection (Week 9)/15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4 17.1 MB
  • 15. Anomaly Detection (Week 9)/docs-slides-Lecture15.pdf 3.5 MB
  • 15. Anomaly Detection (Week 9)/docs-slides-Lecture15.pptx 6.3 MB
  • 16. Recommender Systems (Week 9)/16 - 1 - Problem Formulation (8 min).mp4 11.2 MB
  • 16. Recommender Systems (Week 9)/16 - 2 - Content Based Recommendations (15 min).mp4 17.8 MB
  • 16. Recommender Systems (Week 9)/16 - 3 - Collaborative Filtering (10 min).mp4 12.3 MB
  • 16. Recommender Systems (Week 9)/16 - 4 - Collaborative Filtering Algorithm (9 min).mp4 10.8 MB
  • 16. Recommender Systems (Week 9)/16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).mp4 10.2 MB
  • 16. Recommender Systems (Week 9)/16 - 6 - Implementational Detail- Mean Normalization (9 min).mp4 10.2 MB
  • 16. Recommender Systems (Week 9)/docs-slides-Lecture16.pdf 1.5 MB
  • 16. Recommender Systems (Week 9)/docs-slides-Lecture16.pptx 3.8 MB
  • 17. Large Scale Machine Learning (Week 10)/17 - 1 - Learning With Large Datasets (6 min).mp4 6.8 MB
  • 17. Large Scale Machine Learning (Week 10)/17 - 2 - Stochastic Gradient Descent (13 min).mp4 16.1 MB
  • 17. Large Scale Machine Learning (Week 10)/17 - 3 - Mini-Batch Gradient Descent (6 min).mp4 7.7 MB
  • 17. Large Scale Machine Learning (Week 10)/17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4 14.0 MB
  • 17. Large Scale Machine Learning (Week 10)/17 - 5 - Online Learning (13 min).mp4 15.6 MB
  • 17. Large Scale Machine Learning (Week 10)/17 - 6 - Map Reduce and Data Parallelism (14 min).mp4 16.8 MB
  • 17. Large Scale Machine Learning (Week 10)/docs-slides-Lecture17.pdf 2.1 MB
  • 17. Large Scale Machine Learning (Week 10)/docs-slides-Lecture17.pptx 4.0 MB
  • 18. Application Example Photo OCR/18 - 1 - Problem Description and Pipeline (7 min).mp4 8.3 MB
  • 18. Application Example Photo OCR/18 - 2 - Sliding Windows (15 min).mp4 17.3 MB
  • 18. Application Example Photo OCR/18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4 19.7 MB
  • 18. Application Example Photo OCR/18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).mp4 16.9 MB
  • 18. Application Example Photo OCR/docs-slides-Lecture18.pdf 2.1 MB
  • 18. Application Example Photo OCR/docs-slides-Lecture18.pptx 6.4 MB
  • 19. Conclusion/19 - 1 - Summary and Thank You (5 min).mp4 6.4 MB
  • _ Coursera.pdf 217.0 kB
  • Homeworks/01. Introduction.pdf 93.2 kB
  • Homeworks/02. Linear regression with one variable.pdf 619.4 kB
  • Homeworks/03. Linear Algebra.pdf 657.9 kB
  • Homeworks/04. Linear Regression with Multiple Variables.pdf 579.1 kB
  • Homeworks/05. Octave Tutorial.pdf 660.8 kB
  • Homeworks/06. Logistic Regression.pdf 692.1 kB
  • Homeworks/07. Regularization.pdf 624.2 kB
  • Homeworks/08. Neural Networks Representation.pdf 1.1 MB
  • Homeworks/09. Neural Networks Learning.pdf 619.7 kB
  • Homeworks/10. Advice for Applying Machine Learning.pdf 295.5 kB
  • Homeworks/11. Machine Learning System Design.pdf 583.2 kB
  • Homeworks/12. Support Vector Machines.pdf 2.0 MB
  • Homeworks/13. Clustering.pdf 591.6 kB
  • Homeworks/14. Anomaly Detection.pdf 646.7 kB
  • Homeworks/15. Principal Component Analysis.pdf 1.1 MB
  • Homeworks/16. Recommender Systems.pdf 704.8 kB
  • Homeworks/17. Large Scale Machine Learning.pdf 626.1 kB
  • Homeworks/18. Application Photo OCR.pdf 700.2 kB
  • Homeworks/View Review Questions _ Coursera.pdf 150.9 kB
  • Programming Assignments/Assignment Details _ Coursera.pdf 56.9 kB
  • Programming Assignments/List Assignments _ Coursera.pdf 197.3 kB
  • Programming Assignments/mlclass-ex1-004.zip 475.4 kB
  • Programming Assignments/mlclass-ex2-004.zip 243.9 kB
  • Programming Assignments/mlclass-ex3-004.zip 7.9 MB
  • Programming Assignments/mlclass-ex4-004.zip 8.0 MB
  • Programming Assignments/mlclass-ex5-004.zip 176.7 kB
  • Programming Assignments/mlclass-ex6-004.zip 914.5 kB
  • Programming Assignments/mlclass-ex7-004.zip 11.6 MB
  • Programming Assignments/mlclass-ex8-004.zip 810.0 kB
  • small-icon.hover.png 26.2 kB
  • Wiki - Course FAQ _ Coursera.pdf 100.4 kB
  • Wiki - Course Information _ Coursera.pdf 83.5 kB
  • Wiki - Course Schedule _ Coursera.pdf 72.1 kB
  • Wiki - Octave __ Matlab Tutorial _ Coursera.pdf 907.4 kB
  • Wiki - Tutoring _ Coursera.pdf 2.4 MB

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