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Machine Learning [2012, ENG]

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I. Introduction (Week 1)

1 - 1 - Welcome (7 min).mp4	MP4	11.95 MB
1 - 1 - Welcome (7 min).srt	SRT	9.86 KB
1 - 2 - What is Machine Learning (7 min).mp4	MP4	9.35 MB
1 - 2 - What is Machine Learning- (7 min).srt	SRT	10.14 KB
1 - 3 - Supervised Learning (12 min).mp4	MP4	13.45 MB
1 - 3 - Supervised Learning (12 min).srt	SRT	16.82 KB
1 - 4 - Unsupervised Learning (14 min).mp4	MP4	16.66 MB
1 - 4 - Unsupervised Learning (14 min).srt	SRT	29.06 KB
docs_slides_Lecture1.pdf	PDF	3.3 MB
docs_slides_Lecture1.pptx	PPTX	4.02 MB

II. Linear Regression with One Variable (Week 1)

2 - 1 - Model Representation (8 min).mp4	MP4	9 MB
2 - 1 - Model Representation (8 min).srt	SRT	9.92 KB
2 - 2 - Cost Function (8 min).mp4	MP4	9.05 MB
2 - 2 - Cost Function (8 min).srt	SRT	9.86 KB
2 - 3 - Cost Function - Intuition I (11 min).mp4	MP4	12.24 MB
2 - 3 - Cost Function - Intuition I (11 min).srt	SRT	12.14 KB
2 - 4 - Cost Function - Intuition II (9 min).mp4	MP4	11.36 MB
2 - 4 - Cost Function - Intuition II (9 min).srt	SRT	11.17 KB
2 - 5 - Gradient Descent (11 min).mp4	MP4	13.5 MB
2 - 5 - Gradient Descent (11 min).srt	SRT	15.39 KB
2 - 6 - Gradient Descent Intuition (12 min).mp4	MP4	13.03 MB
2 - 6 - Gradient Descent Intuition (12 min).srt	SRT	15.5 KB
2 - 7 - Gradient Descent For Linear Regression (10 min).mp4	MP4	12.18 MB
2 - 7 - Gradient Descent For Linear Regression (10 min).srt	SRT	18.71 KB
2 - 8 - What's Next (6 min).srt	SRT	8.45 KB
2 - 8 - Whats Next (6 min).mp4	MP4	6.08 MB
docs_slides_Lecture2.pdf	PDF	2.88 MB
docs_slides_Lecture2.pptx	PPTX	5.35 MB

III. Linear Algebra Review (Week 1, Optional)

3 - 1 - Matrices and Vectors (9 min).mp4	MP4	9.56 MB
3 - 1 - Matrices and Vectors (9 min).srt	SRT	15.88 KB
3 - 1 - Matrices and Vectors (9 min).txt	TXT	7.08 KB
3 - 2 - Addition and Scalar Multiplication (7 min).mp4	MP4	7.46 MB
3 - 2 - Addition and Scalar Multiplication (7 min).srt	SRT	11.97 KB
3 - 3 - Matrix Vector Multiplication (14 min).mp4	MP4	15 MB
3 - 3 - Matrix Vector Multiplication (14 min).srt	SRT	24.26 KB
3 - 4 - Matrix Matrix Multiplication (11 min).mp4	MP4	12.59 MB
3 - 4 - Matrix Matrix Multiplication (11 min).srt	SRT	20.6 KB
3 - 5 - Matrix Multiplication Properties (9 min).mp4	MP4	9.81 MB
3 - 5 - Matrix Multiplication Properties (9 min).srt	SRT	16.75 KB
3 - 6 - Inverse and Transpose (11 min).mp4	MP4	12.87 MB
3 - 6 - Inverse and Transpose (11 min).srt	SRT	21.09 KB
docs_slides_Lecture3.pdf	PDF	1.8 MB
docs_slides_Lecture3.pptx	PPTX	4.92 MB

IV. Linear Regression with Multiple Variables (Week 2)

4 - 1 - Multiple Features (8 min).mp4	MP4	8.84 MB
4 - 1 - Multiple Features (8 min).srt	SRT	14.55 KB
4 - 2 - Gradient Descent for Multiple Variables (5 min).mp4	MP4	5.78 MB
4 - 2 - Gradient Descent for Multiple Variables (5 min).srt	SRT	6.63 KB
4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mp4	MP4	9.46 MB
4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).srt	SRT	17 KB
4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mp4	MP4	9.26 MB
4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).srt	SRT	18.49 KB
4 - 5 - Features and Polynomial Regression (8 min).mp4	MP4	8.26 MB
4 - 5 - Features and Polynomial Regression (8 min).srt	SRT	15.9 KB
4 - 6 - Normal Equation (16 min).mp4	MP4	17.13 MB
4 - 6 - Normal Equation (16 min).srt	SRT	31.2 KB
4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mp4	MP4	6.24 MB
4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).srt	SRT	9.76 KB
docs_slides_Lecture4.pdf	PDF	1.7 MB
docs_slides_Lecture4.pptx	PPTX	4.4 MB
ex1.zip	ZIP	469.78 KB

IX. Neural Networks Learning (Week 5)

9 - 1 - Cost Function (7 min).mp4	MP4	7.66 MB
9 - 1 - Cost Function (7 min).srt	SRT	13.16 KB
9 - 2 - Backpropagation Algorithm (12 min).mp4	MP4	13.94 MB
9 - 2 - Backpropagation Algorithm (12 min).srt	SRT	22.83 KB
9 - 3 - Backpropagation Intuition (13 min).mp4	MP4	15.44 MB
9 - 3 - Backpropagation Intuition (13 min).srt	SRT	24.96 KB
9 - 4 - Implementation Note Unrolling Parameters (8 min).mp4	MP4	9.38 MB
9 - 4 - Implementation Note- Unrolling Parameters (8 min).srt	SRT	14.9 KB
9 - 5 - Gradient Checking (12 min).mp4	MP4	13.5 MB
9 - 5 - Gradient Checking (12 min).srt	SRT	23.58 KB
9 - 6 - Random Initialization (7 min).mp4	MP4	7.56 MB
9 - 6 - Random Initialization (7 min).srt	SRT	13.95 KB
9 - 7 - Putting It Together (14 min).mp4	MP4	16.3 MB
9 - 7 - Putting It Together (14 min).srt	SRT	27.68 KB
9 - 8 - Autonomous Driving (7 min).mp4	MP4	14.88 MB
9 - 8 - Autonomous Driving (7 min).srt	SRT	9.8 KB
docs_slides_Lecture9.pdf	PDF	3.37 MB
docs_slides_Lecture9.pptx	PPTX	4.96 MB
ex4.zip	ZIP	7.58 MB

V. Octave Tutorial (Week 2)

5 - 1 - Basic Operations (14 min).mp4	MP4	17.72 MB
5 - 1 - Basic Operations (14 min).srt	SRT	25.35 KB
5 - 2 - Moving Data Around (16 min).mp4	MP4	20.77 MB
5 - 2 - Moving Data Around (16 min).srt	SRT	28.61 KB
5 - 3 - Computing on Data (13 min).mp4	MP4	15.25 MB
5 - 3 - Computing on Data (13 min).srt	SRT	24.95 KB
5 - 4 - Plotting Data (10 min).mp4	MP4	13.32 MB
5 - 4 - Plotting Data (10 min).srt	SRT	17.36 KB
5 - 5 - Control Statements for while if statements (13 min).mp4	MP4	16.49 MB
5 - 5 - Control Statements- for, while, if statements (13 min).srt	SRT	23.38 KB
5 - 6 - Vectorization (14 min).mp4	MP4	16.09 MB
5 - 6 - Vectorization (14 min).srt	SRT	25.23 KB
5 - 7 - Working on and Submitting Programming Exercises (4 min).mp4	MP4	5.46 MB
5 - 7 - Working on and Submitting Programming Exercises (4 min).srt	SRT	4.42 KB
docs_slides_Lecture5.pdf	PDF	242.37 KB
docs_slides_Lecture5.pptx	PPTX	407.28 KB

VI. Logistic Regression (Week 3)

6 - 1 - Classification (8 min).mp4	MP4	8.77 MB
6 - 1 - Classification (8 min).srt	SRT	16.18 KB
6 - 2 - Hypothesis Representation (7 min).mp4	MP4	8.34 MB
6 - 2 - Hypothesis Representation (7 min).srt	SRT	14.15 KB
6 - 3 - Decision Boundary (15 min).mp4	MP4	16.74 MB
6 - 3 - Decision Boundary (15 min).srt	SRT	26.75 KB
6 - 4 - Cost Function (11 min).mp4	MP4	13.09 MB
6 - 4 - Cost Function (11 min).srt	SRT	22.17 KB
6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mp4	MP4	11.96 MB
6 - 5 - Simplified Cost Function and Gradient Descent (10 min).srt	SRT	19.58 KB
6 - 6 - Advanced Optimization (14 min).mp4	MP4	18.15 MB
6 - 6 - Advanced Optimization (14 min).srt	SRT	27.8 KB
6 - 7 - Multiclass Classification One-vs-all (6 min).mp4	MP4	6.93 MB
6 - 7 - Multiclass Classification- One-vs-all (6 min).srt	SRT	12.61 KB
docs_slides_Lecture6.pdf	PDF	2.12 MB
docs_slides_Lecture6.pptx	PPTX	3.82 MB

VII. Regularization (Week 3)

7 - 1 - The Problem of Overfitting (10 min).mp4	MP4	11.15 MB
7 - 1 - The Problem of Overfitting (10 min).srt	SRT	19.26 KB
7 - 2 - Cost Function (10 min).mp4	MP4	11.63 MB
7 - 2 - Cost Function (10 min).srt	SRT	19.73 KB
7 - 3 - Regularized Linear Regression (11 min).mp4	MP4	12 MB
7 - 3 - Regularized Linear Regression (11 min).srt	SRT	20.37 KB
7 - 4 - Regularized Logistic Regression (9 min).mp4	MP4	10.89 MB
7 - 4 - Regularized Logistic Regression (9 min).srt	SRT	17.16 KB
docs_slides_Lecture7.pdf	PDF	2.34 MB
docs_slides_Lecture7.pptx	PPTX	2.59 MB
ex2.zip	ZIP	243.02 KB

VIII. Neural Networks Representation (Week 4)

8 - 1 - Non-linear Hypotheses (10 min).mp4	MP4	10.88 MB
8 - 1 - Non-linear Hypotheses (10 min).srt	SRT	19.05 KB
8 - 2 - Neurons and the Brain (8 min).mp4	MP4	9.89 MB
8 - 2 - Neurons and the Brain (8 min).srt	SRT	16.41 KB
8 - 3 - Model Representation I (12 min).mp4	MP4	13.51 MB
8 - 3 - Model Representation I (12 min).srt	SRT	21.6 KB
8 - 4 - Model Representation II (12 min).mp4	MP4	13.45 MB
8 - 4 - Model Representation II (12 min).srt	SRT	22.43 KB
8 - 5 - Examples and Intuitions I (7 min).mp4	MP4	7.89 MB
8 - 5 - Examples and Intuitions I (7 min).srt	SRT	13.05 KB
8 - 6 - Examples and Intuitions II (10 min).mp4	MP4	14 MB
8 - 6 - Examples and Intuitions II (10 min).srt	SRT	17.05 KB
8 - 7 - Multiclass Classification (4 min).mp4	MP4	4.83 MB
8 - 7 - Multiclass Classification (4 min).srt	SRT	7.43 KB
docs_slides_Lecture8.pdf	PDF	4.97 MB
docs_slides_Lecture8.pptx	PPTX	40.36 MB
ex3.zip	ZIP	7.52 MB

X. Advice for Applying Machine Learning (Week 6)

10 - 1 - Deciding What to Try Next (6 min).mp4	MP4	6.86 MB
10 - 1 - Deciding What to Try Next (6 min).srt	SRT	12.44 KB
10 - 2 - Evaluating a Hypothesis (8 min).mp4	MP4	8.48 MB
10 - 2 - Evaluating a Hypothesis (8 min).srt	SRT	11.5 KB
10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mp4	MP4	14.07 MB
10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).srt	SRT	24.64 KB
10 - 4 - Diagnosing Bias vs. Variance (8 min).mp4	MP4	8.97 MB
10 - 4 - Diagnosing Bias vs. Variance (8 min).srt	SRT	16.14 KB
10 - 5 - Regularization and Bias_Variance (11 min).mp4	MP4	12.6 MB
10 - 5 - Regularization and Bias_Variance (11 min).srt	SRT	22.54 KB
10 - 6 - Learning Curves (12 min).mp4	MP4	12.92 MB
10 - 6 - Learning Curves (12 min).srt	SRT	24.73 KB
10 - 7 - Deciding What to Do Next Revisited (7 min).mp4	MP4	8.18 MB
10 - 7 - Deciding What to Do Next Revisited (7 min).srt	SRT	14.08 KB
docs_slides_Lecture10.pdf	PDF	1.48 MB
docs_slides_Lecture10.pptx	PPTX	3.35 MB
ex5.zip	ZIP	177.05 KB

XI. Machine Learning System Design (Week 6)

11 - 1 - Prioritizing What to Work On (10 min).mp4	MP4	11.17 MB
11 - 1 - Prioritizing What to Work On (10 min).srt	SRT	19.66 KB
11 - 2 - Error Analysis (13 min).mp4	MP4	15.43 MB
11 - 2 - Error Analysis (13 min).srt	SRT	27.47 KB
11 - 3 - Error Metrics for Skewed Classes (12 min).mp4	MP4	13.25 MB
11 - 3 - Error Metrics for Skewed Classes (12 min).srt	SRT	22.06 KB
11 - 4 - Trading Off Precision and Recall (14 min).mp4	MP4	15.99 MB
11 - 4 - Trading Off Precision and Recall (14 min).srt	SRT	28.59 KB
11 - 5 - Data For Machine Learning (11 min).mp4	MP4	12.87 MB
11 - 5 - Data For Machine Learning (11 min).srt	SRT	23.16 KB
docs_slides_Lecture11.pdf	PDF	497.64 KB
docs_slides_Lecture11.pptx	PPTX	1.93 MB

XII. Support Vector Machines (Week 7)

12 - 1 - Optimization Objective (15 min).mp4	MP4	16.65 MB
12 - 1 - Optimization Objective (15 min).srt	SRT	29.43 KB
12 - 2 - Large Margin Intuition (11 min).mp4	MP4	11.81 MB
12 - 2 - Large Margin Intuition (11 min).srt	SRT	21.28 KB
12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mp4	MP4	21.83 MB
12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).srt	SRT	35.87 KB
12 - 4 - Kernels I (16 min).mp4	MP4	17.57 MB
12 - 4 - Kernels I (16 min).srt	SRT	29.07 KB
12 - 5 - Kernels II (16 min) (1).mp4	MP4	17.45 MB
12 - 5 - Kernels II (16 min) (1).srt	SRT	30.67 KB
12 - 5 - Kernels II (16 min).mp4	MP4	17.45 MB
12 - 5 - Kernels II (16 min).srt	SRT	30.67 KB
12 - 6 - Using An SVM (21 min).mp4	MP4	23.95 MB
12 - 6 - Using An SVM (21 min).srt	SRT	43.5 KB
docs_slides_Lecture12.pdf	PDF	2.3 MB
docs_slides_Lecture12.pptx	PPTX	5.39 MB
ex6.zip	ZIP	896.41 KB

XIII. Clustering (Week 8)

13 - 1 - Unsupervised Learning Introduction (3 min).mp4	MP4	3.8 MB
13 - 1 - Unsupervised Learning- Introduction (3 min).srt	SRT	6.99 KB
13 - 2 - K-Means Algorithm (13 min).mp4	MP4	13.81 MB
13 - 2 - K-Means Algorithm (13 min).srt	SRT	26.24 KB
13 - 3 - Optimization Objective (7 min).mp4	MP4	8.15 MB
13 - 3 - Optimization Objective (7 min).srt	SRT	13.7 KB
13 - 4 - Random Initialization (8 min).mp4	MP4	8.67 MB
13 - 4 - Random Initialization (8 min).srt	SRT	16.23 KB
13 - 5 - Choosing the Number of Clusters (8 min).mp4	MP4	9.4 MB
13 - 5 - Choosing the Number of Clusters (8 min).srt	SRT	17.94 KB
docs_slides_Lecture13.pdf	PDF	2.17 MB
docs_slides_Lecture13.pptx	PPTX	2.79 MB

XIV. Dimensionality Reduction (Week 8)

14 - 1 - Motivation I Data Compression (10 min).mp4	MP4	14.31 MB
14 - 1 - Motivation I- Data Compression (10 min).srt	SRT	20.14 KB
14 - 2 - Motivation II Visualization (6 min).mp4	MP4	6.3 MB
14 - 2 - Motivation II- Visualization (6 min).srt	SRT	10.18 KB
14 - 3 - Principal Component Analysis Problem Formulation (9 min).mp4	MP4	10.45 MB
14 - 3 - Principal Component Analysis Problem Formulation (9 min).srt	SRT	18.42 KB
14 - 4 - Principal Component Analysis Algorithm (15 min).mp4	MP4	17.79 MB
14 - 4 - Principal Component Analysis Algorithm (15 min).srt	SRT	28.58 KB
14 - 5 - Choosing the Number of Principal Components (11 min).mp4	MP4	11.84 MB
14 - 5 - Choosing the Number of Principal Components (11 min).srt	SRT	21.14 KB
14 - 6 - Reconstruction from Compressed Representation (4 min).mp4	MP4	4.98 MB
14 - 6 - Reconstruction from Compressed Representation (4 min).srt	SRT	7.56 KB
14 - 7 - Advice for Applying PCA (13 min).mp4	MP4	14.7 MB
14 - 7 - Advice for Applying PCA (13 min).srt	SRT	26.32 KB
docs_slides_Lecture14.pdf	PDF	1.61 MB
docs_slides_Lecture14.pptx	PPTX	3.62 MB
ex7.zip	ZIP	11.05 MB

XIX. Conclusion

	19 - 1 - Summary and Thank You (5 min).mp4	MP4	6.09 MB
	19 - 1 - Summary and Thank You (5 min).srt	SRT	8.08 KB

XV. Anomaly Detection (Week 9)

15 - 1 - Problem Motivation (8 min).mp4	MP4	8.35 MB
15 - 1 - Problem Motivation (8 min).srt	SRT	16.02 KB
15 - 2 - Gaussian Distribution (10 min).mp4	MP4	11.69 MB
15 - 2 - Gaussian Distribution (10 min).srt	SRT	20.64 KB
15 - 3 - Algorithm (12 min).mp4	MP4	13.95 MB
15 - 3 - Algorithm (12 min).srt	SRT	23.49 KB
15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mp4	MP4	15.15 MB
15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).srt	SRT	27.29 KB
15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mp4	MP4	9.28 MB
15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).srt	SRT	16.42 KB
15 - 6 - Choosing What Features to Use (12 min).mp4	MP4	14.12 MB
15 - 6 - Choosing What Features to Use (12 min).srt	SRT	25.14 KB
15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mp4	MP4	15.93 MB
15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).srt	SRT	27.44 KB
15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mp4	MP4	16.34 MB
15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).srt	SRT	26.26 KB
docs_slides_Lecture15.pdf	PDF	3.33 MB
docs_slides_Lecture15.pptx	PPTX	6.05 MB

XVI. Recommender Systems (Week 9)

16 - 1 - Problem Formulation (8 min).mp4	MP4	10.67 MB
16 - 1 - Problem Formulation (8 min).srt	SRT	16.82 KB
16 - 2 - Content Based Recommendations (15 min).mp4	MP4	16.93 MB
16 - 2 - Content Based Recommendations (15 min).srt	SRT	28.57 KB
16 - 3 - Collaborative Filtering (10 min).mp4	MP4	11.75 MB
16 - 3 - Collaborative Filtering (10 min).srt	SRT	20.24 KB
16 - 4 - Collaborative Filtering Algorithm (9 min).mp4	MP4	10.31 MB
16 - 4 - Collaborative Filtering Algorithm (9 min).srt	SRT	16.49 KB
16 - 5 - Vectorization Low Rank Matrix Factorization (8 min).mp4	MP4	9.68 MB
16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min).srt	SRT	16.31 KB
16 - 6 - Implementational Detail Mean Normalization (9 min).mp4	MP4	9.71 MB
16 - 6 - Implementational Detail- Mean Normalization (9 min).srt	SRT	16.58 KB
docs_slides_Lecture16.pdf	PDF	1.42 MB
docs_slides_Lecture16.pptx	PPTX	3.6 MB
ex8.zip	ZIP	794.8 KB

XVII. Large Scale Machine Learning (Week 10)

17 - 1 - Learning With Large Datasets (6 min).mp4	MP4	6.5 MB
17 - 1 - Learning With Large Datasets (6 min).srt	SRT	7.86 KB
17 - 2 - Stochastic Gradient Descent (13 min).mp4	MP4	15.33 MB
17 - 2 - Stochastic Gradient Descent (13 min).srt	SRT	18.15 KB
17 - 3 - Mini-Batch Gradient Descent (6 min).mp4	MP4	7.32 MB
17 - 3 - Mini-Batch Gradient Descent (6 min).srt	SRT	7.78 KB
17 - 4 - Stochastic Gradient Descent Convergence (12 min).mp4	MP4	13.33 MB
17 - 4 - Stochastic Gradient Descent Convergence (12 min).srt	SRT	16.16 KB
17 - 5 - Online Learning (13 min).mp4	MP4	14.91 MB
17 - 5 - Online Learning (13 min).srt	SRT	27.68 KB
17 - 6 - Map Reduce and Data Parallelism (14 min).mp4	MP4	16.06 MB
17 - 6 - Map Reduce and Data Parallelism (14 min).srt	SRT	28.86 KB
docs_slides_Lecture17.pdf	PDF	1.98 MB
docs_slides_Lecture17.pptx	PPTX	3.78 MB

XVIII. Application Example Photo OCR

18 - 1 - Problem Description and Pipeline (7 min).mp4	MP4	7.91 MB
18 - 1 - Problem Description and Pipeline (7 min).srt	SRT	14.71 KB
18 - 2 - Sliding Windows (15 min).mp4	MP4	16.52 MB
18 - 2 - Sliding Windows (15 min).srt	SRT	31.47 KB
18 - 3 - Getting Lots of Data and Artificial Data (16 min).mp4	MP4	18.82 MB
18 - 3 - Getting Lots of Data and Artificial Data (16 min).srt	SRT	35.19 KB
18 - 4 - Ceiling Analysis What Part of the Pipeline to Work on Next (14 min).mp4	MP4	16.11 MB
18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min).srt	SRT	30.5 KB
docs_slides_Lecture18.pdf	PDF	1.97 MB
docs_slides_Lecture18.pptx	PPTX	6.13 MB

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Machine Learning [2012, ENG]

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