Machine learning coursera

Alireza Saberi
107 videos
687,912 views
Last updated on Dec 22, 2014

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			Machine learning W1 01: Welcome by Alireza Saberi			6:55
			Machine learning W1 02: What is Machine Learning by Alireza Saberi			7:15
			Machine learning W1 03 Supervised Learning by Alireza Saberi			12:30
			Machine learning W1 04 Unsupervised Learning by Alireza Saberi			14:14
			Machine learning W1 05 Model Representation by Alireza Saberi			8:11
			Machine learning W1 06 Cost Function 1 by Alireza Saberi			8:13
			Machine learning W1 07 Cost Function 2 by Alireza Saberi			8:49
			Machine learning W1 08 Gradient Descent 1 by Alireza Saberi			11:31
			Machine learning W1 09 Gradient Descent 2 by Alireza Saberi			11:51
			Machine learning W1 10 Gradient Descent For Linear Regression by Alireza Saberi			10:21
			Machine learning W1 11 What 's Next by Alireza Saberi			5:50
			Machine learning W1 12 Matrices and Vectors by Alireza Saberi			8:46
			Machine learning W1 13 Addition and Scalar Multiplication by Alireza Saberi			6:54
			Machine learning W1 14 Matrix Vector Multiplication by Alireza Saberi			13:40
			Machine learning W1 15 Matrix Vector Multiplication by Alireza Saberi			13:40
			Machine learning W1 16 Matrix Matrix Multiplication by Alireza Saberi			11:10
			Machine learning W1 17 Matrix Multiplication Properties by Alireza Saberi			9:03
			Machine learning W1 18 Inverse and Transpose by Alireza Saberi			11:14
			Machine learning W2 01 Multiple Features by Alireza Saberi			8:23
			Machine learning W1 02 Gradient Descent for Multiple Variables by Alireza Saberi			5:05
			Machine learning W2 03 Gradient Descent in Practice I Feature Scaling by Alireza Saberi			8:53
			Machine learning W2 04 Gradient Descent in Practice II Learning Rate by Alireza Saberi			8:59
			Machine learning W2 05 Features and Polynomial Regression by Alireza Saberi			7:40
			Machine learning W2 06 Normal Equation by Alireza Saberi			16:18
			Machine learning W2 07 Normal Equation Noninvertibility Optional by Alireza Saberi			5:59
			Machine learning W2 08 Basic Operations by Alireza Saberi			14:00
			Machine learning W2 09 Moving Data Around by Alireza Saberi			16:08
			Machine learning W2 10 Computing on Data by Alireza Saberi			13:15
			Machine learning W2 11 Plotting Data by Alireza Saberi			9:39
			Machine learning W2 12 Control Statements for, while, if statements by Alireza Saberi			12:56
			Machine learning W2 13 Vectorization by Alireza Saberi			13:49
			Machine learning W2 14 Working on and Submitting Programming Exercises by Alireza Saberi			3:34
			Machine learning W3 1 Classification by Alireza Saberi			8:09
			Machine learning W3 2 Hypothesis Representation by Alireza Saberi			7:25
			Machine learning W3 3 Decision Boundary by Alireza Saberi			14:50
			Machine learning W3 4 Cost Function by Alireza Saberi			11:26
			Machine learning W3 5 Simplified Cost Function and Gradient Descent by Alireza Saberi			10:15
			Machine learning W3 6 Advanced Optimization by Alireza Saberi			14:07
			Machine learning W3 7 Multiclass Classification One vs all by Alireza Saberi			6:16
			Machine learning W3 8 The Problem of Overfitting by Alireza Saberi			9:43
			Machine learning W3 9 Cost Function by Alireza Saberi			10:11
			Machine learning W3 10 Regularized Linear Regression by Alireza Saberi			10:41
			Machine learning W3 11 Regularized Logistic Regression by Alireza Saberi			8:34
			Machine learning W4 1 Non linear Hypotheses by Alireza Saberi			9:36
			Machine learning W4 2 Neurons and the Brain by Alireza Saberi			7:48
			Machine learning W4 3 Model Representation I by Alireza Saberi			12:02
			Machine learning W4 4 Model Representation II by Alireza Saberi			11:47
			Machine learning W4 5 Examples and Intuitions I by Alireza Saberi			7:16
			Machine learning W4 6 Examples and Intuitions II by Alireza Saberi			10:21
			Machine learning W4 7 Multiclass Classification by Alireza Saberi			3:52
			Machine learning W5 1 Cost Function by Alireza Saberi			6:44
			Machine learning W5 2 Backpropagation Algorithm by Alireza Saberi			12:00
			Machine learning W5 3 Backpropagation Intuition by Alireza Saberi			12:45
			Machine learning W5 4 Implementation Note Unrolling Parameters by Alireza Saberi			7:48
			Machine learning W5 5 Gradient Checking by Alireza Saberi			11:38
			Machine learning W5 6 Random Initialization by Alireza Saberi			6:52
			Machine learning W5 7 Putting It Together by Alireza Saberi			13:24
			Machine learning W5 8 Autonomous Driving by Alireza Saberi			6:31
			Machine learning W6 1 Deciding What to Try Next by Alireza Saberi			5:51
			Machine learning W6 2 Evaluating a Hypothesis 8 min by Alireza Saberi			7:36
			Machine learning W6 3 Model Selection and Train Validation Test Sets by Alireza Saberi			12:04
			Machine learning W6 4 Diagnosing Bias vs Variance by Alireza Saberi			7:43
			Machine learning W6 5 Regularization and Bias Variance by Alireza Saberi			11:21
			Machine learning W6 6 Learning Curves by Alireza Saberi			11:54
			Machine learning W6 7 Deciding What to Do Next Revisited by Alireza Saberi			6:51
			Machine learning W6 8 Prioritizing What to Work On by Alireza Saberi			9:30
			Machine learning W6 9 Error Analysis by Alireza Saberi			13:13
			Machine learning W6 10 Error Metrics for Skewed Classes by Alireza Saberi			11:36
			Machine learning W6 11 Trading Off Precision and Recall by Alireza Saberi			14:06
			Machine learning W6 12 Data For Machine Learning by Alireza Saberi			11:10
			Machine learning W7 1 Optimization Objective by Alireza Saberi			14:48
			Machine learning W7 2 Large Margin Intuition by Alireza Saberi			10:37
			Machine learning W7 3 Mathematics Behind Large Margin Classification Optional by Alireza Saberi			19:42
			Machine learning W7 4 Kernels I by Alireza Saberi			15:45
			Machine learning W7 5 Kernels II by Alireza Saberi			15:44
			Machine learning W7 6 Using An SVM by Alireza Saberi			21:03
			Machine learning W8 1 Unsupervised Learning Introduction by Alireza Saberi			3:18
			Machine learning W8 2 K Means Algorithm by Alireza Saberi			12:33
			Machine learning W8 3 Optimization Objective by Alireza Saberi			7:05
			Machine learning W8 4 Random Initialization by Alireza Saberi			7:50
			Machine learning W8 5 Choosing the Number of Clusters by Alireza Saberi			8:23
			Machine learning W8 6 Motivation I Data Compression by Alireza Saberi			10:10
			Machine learning W8 7 Motivation II Visualization by Alireza Saberi			5:28
			Machine learning W8 8 Principal Component Analysis Problem Formulation by Alireza Saberi			9:06
			Machine learning W8 9 Principal Component Analysis Algorithm by Alireza Saberi			15:15
			Machine learning W8 10 Choosing the Number of Principal Components by Alireza Saberi			10:31
			Machine learning W8 11 Reconstruction from Compressed Representation by Alireza Saberi			3:55
			Machine learning W8 12 Advice for Applying PCA by Alireza Saberi			12:49
			Machine learning W9 1 Problem Motivation by Alireza Saberi			7:39
			Machine learning W9 2 Gaussian Distribution by Alireza Saberi			10:28
			Machine learning W9 3 Algorithm by Alireza Saberi			12:03
			Machine learning W9 4 Developing and Evaluating an Anomaly Detection System by Alireza Saberi			13:08
			Machine learning W9 5 Anomaly Detection vs Supervised Learning by Alireza Saberi			7:37
			Machine learning W9 6 Choosing What Features to Use by Alireza Saberi			12:18
			Machine learning W9 7 Multivariate Gaussian Distribution Optional by Alireza Saberi			13:46
			Machine learning W9 8 Anomaly Detection using the Multivariate Gaussian Distribution Optional by Alireza Saberi			14:04
			Machine learning W10 1 Learning With Large Datasets by Alireza Saberi			5:46
			Machine learning W10 2 Stochastic Gradient Descent by Alireza Saberi			13:20
			Machine learning W10 3 Mini Batch Gradient Descent by Alireza Saberi			6:18
			Machine learning W10 4 Stochastic Gradient Descent Convergence by Alireza Saberi			11:32

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