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Lecture notes
Programming assignment 1
Week 1 Introduction & Linear Regression with One Variable
This method looks at every example in the entire training set on every step, and is called batch gradient descent.
Model and Cost Function
Cost Function
https://www.coursera.org/learn/machine-learning/lecture/rkTp3/cost-function
Cost Function
https://www.coursera.org/learn/machine-learning/supplement/nhzyF/cost-function
Cost Function - Intuition I
https://www.coursera.org/learn/machine-learning/lecture/N09c6/cost-function-intuition-i
Cost Function - Intuition I
https://www.coursera.org/learn/machine-learning/supplement/u3qF5/cost-function-intuition-i
Parameter Learning
Gradient Descent
https://www.coursera.org/learn/machine-learning/lecture/8SpIM/gradient-descent
Gradient Descent
https://www.coursera.org/learn/machine-learning/supplement/2GnUg/gradient-descent
Gradient Descent Intuition
https://www.coursera.org/learn/machine-learning/supplement/QKEdR/gradient-descent-intuition
Gradient Descent For Linear Regression
Week 2
Feature scaling involves dividing the input values by the range (i.e. the maximum value minus the minimum value) of the input variable, resulting in a new range of just 1.
Mean normalization involves subtracting the average value for an input variable from the values for that input variable resulting in a new average value for the input variable of just zero. To implement both of these techniques, adjust your input values as shown in this formula:
$x_i :=\frac{x_i - \mu_i}{s_i}$
Where $μ_i$ is the average of all the values for feature (i) and $s_i$ is the range of values (max - min), or $s_i$ is the standard deviation.
Linear Regression with Multiple Variables
Environment Setup Instructions
Setting Up Your Programming Assignment Environment8 min
Installing MATLAB3 min
Installing Octave on Windows3 min
Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10 min
Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3 min
Installing Octave on GNU/Linux7 min
More Octave/MATLAB resources 10 min
https://www.coursera.org/learn/machine-learning/supplement/Mlf3e/more-octave-matlab-resources
Multivariate Linear Regression
Multiple Features8 min
Multiple Features3 min
Gradient Descent for Multiple Variables5 min
Gradient Descent For Multiple Variables2 min
Gradient Descent in Practice I - Feature Scaling8 min
Gradient Descent in Practice I - Feature Scaling3 min
Gradient Descent in Practice II - Learning Rate8 min
Gradient Descent in Practice II - Learning Rate4 min
Features and Polynomial Regression7 min
Features and Polynomial Regression3 min
Computing Parameters Analytically
Normal Equation16 min
Normal Equation3 min
Normal Equation Noninvertibility5 min
Normal Equation Noninvertibility2 min
Submitting Programming Assignments
Working on and Submitting Programming Assignments3 min
Programming tips from Mentors10 min
Review
Lecture Slides20 min
Quiz: Linear Regression with Multiple Variables5 questions
Octave/Matlab Tutorial
Octave/Matlab Tutorial
Basic Operations13 min
Moving Data Around16 min
Computing on Data13 min
Plotting Data9 min
Control Statements: for, while, if statement12 min
Vectorization13 min
Review
Lecture Slides10 min
The course has ended. Assignments may not be resubmitted.
Quiz: Octave/Matlab Tutorial5 questions
Programming Assignment: Linear Regression3h
Week 3
Logistic Regression
Classification and Representation
Classification8 min
Classification2 min
Hypothesis Representation7 min
Hypothesis Representation3 min
Decision Boundary14 min
Decision Boundary3 min
Logistic Regression Model
Cost Function10 min
Cost Function3 min
Simplified Cost Function and Gradient Descent10 min
Simplified Cost Function and Gradient Descent3 min
Advanced Optimization14 min
Advanced Optimization3 min
Multiclass Classification
Multiclass Classification: One-vs-all6 min
Multiclass Classification: One-vs-all3 min
Review
Lecture Slides 10 min
https://www.coursera.org/learn/machine-learning/supplement/QEYX8/lecture-slides
Quiz: Logistic Regression 5 questions
Regularization
Solving the Problem of Overfitting
The Problem of Overfitting9 min
The Problem of Overfitting3 min
Cost Function10 min
Cost Function3 min
Regularized Linear Regression10 min
Regularized Linear Regression3 min
Regularized Logistic Regression8 min
Regularized Logistic Regression3 min
Review
Lecture Slides10 min
The course has ended. Assignments may not be resubmitted.
Quiz: Regularization5 questions
Programming Assignment: Logistic Regression3h
Week 4
Neural Networks: Representation
Motivations
Non-linear Hypotheses9 min
Neurons and the Brain7 min
Neural Networks
Model Representation I12 min
Model Representation I6 min
Model Representation II11 min
Model Representation II6 min
Applications
Examples and Intuitions I7 min
Examples and Intuitions I2 min
Examples and Intuitions II10 min
Examples and Intuitions II3 min
Multiclass Classification3 min
Multiclass Classification3 min
Review
Lecture Slides10 min
The course has ended. Assignments may not be resubmitted.
Quiz: Neural Networks: Representation5 questions
Programming Assignment: Multi-class Classification and Neural Networks3h
Week 5
Neural Networks: Learning
Cost Function and Backpropagation
Cost Function6 min
Cost Function4 min
Backpropagation Algorithm11 min
Backpropagation Algorithm10 min
Backpropagation Intuition12 min
Backpropagation Intuition4 min
Backpropagation in Practice
Implementation Note: Unrolling Parameters7 min
Implementation Note: Unrolling Parameters3 min
Gradient Checking11 min
Gradient Checking3 min
Random Initialization6 min
Random Initialization3 min
Putting It Together13 min
Putting It Together4 min
Application of Neural Networks
Autonomous Driving6 min
Review
Lecture Slides10 min
https://www.coursera.org/learn/machine-learning/supplement/FklyY/lecture-slides
The course has ended. Assignments may not be resubmitted.
Quiz: Neural Networks: Learning5 questions
Programming Assignment: Neural Network Learning3h
Week 6
Advice for Applying Machine Learning
Evaluating a Learning Algorithm
Deciding What to Try Next5 min
Evaluating a Hypothesis7 min
Evaluating a Hypothesis4 min
Model Selection and Train/Validation/Test Sets12 min
Model Selection and Train/Validation/Test Sets3 min
Bias vs. Variance
Diagnosing Bias vs. Variance7 min
Diagnosing Bias vs. Variance3 min
Regularization and Bias/Variance11 min
Regularization and Bias/Variance3 min
Learning Curves11 min
Learning Curves3 min
Deciding What to Do Next Revisited6 min
Deciding What to do Next Revisited3 min
Review
Lecture Slides 10 min
https://www.coursera.org/learn/machine-learning/supplement/7BHrF/lecture-slides
The course has ended. Assignments may not be resubmitted.
Quiz: Advice for Applying Machine Learning5 questions
Programming Assignment: Regularized Linear Regression and Bias/Variance3h
Machine Learning System Design
Building a Spam Classifier
Prioritizing What to Work On9 min
Prioritizing What to Work On3 min
Error Analysis13 min
Error Analysis3 min
Handling Skewed Data
Error Metrics for Skewed Classes11 min
Trading Off Precision and Recall14 min
Using Large Data Sets
Data For Machine Learning11 min
Review
Lecture Slides 10 min
https://www.coursera.org/learn/machine-learning/supplement/gFC7y/lecture-slides
Quiz: Machine Learning System Design5 questions
Week 7
Support Vector Machines
Large Margin Classification
Optimization Objective14 min
Large Margin Intuition10 min
Mathematics Behind Large Margin Classification19 min
Kernels
Kernels I15 min
Kernels II15 min
SVMs in Practice
Using An SVM21 min
Review
Lecture Slides 10 min
https://www.coursera.org/learn/machine-learning/supplement/pSe2X/lecture-slides
The course has ended. Assignments may not be resubmitted.
Quiz: Support Vector Machines5 questions
Programming Assignment: Support Vector Machines3h
Week 8
Unsupervised Learning
Clustering
Unsupervised Learning: Introduction3 min
K-Means Algorithm12 min
Optimization Objective7 min
Random Initialization7 min
Choosing the Number of Clusters8 min
Review
Lecture Slides 10 min
https://www.coursera.org/learn/machine-learning/supplement/hFF7A/lecture-slides
Quiz: Unsupervised Learning5 questions
Dimensionality Reduction
Motivation
Motivation I: Data Compression10 min
Motivation II: Visualization5 min
Principal Component Analysis
Principal Component Analysis Problem Formulation9 min
Principal Component Analysis Algorithm15 min
Applying PCA
Reconstruction from Compressed Representation3 min
Choosing the Number of Principal Components10 min
Advice for Applying PCA12 min
Review
Lecture Slides 10 min
https://www.coursera.org/learn/machine-learning/supplement/SCJi4/lecture-slides
The course has ended. Assignments may not be resubmitted.
Quiz: Principal Component Analysis5 questions
Programming Assignment: K-Means Clustering and PCA 3h
Week 9
Anomaly Detection
Density Estimation
Problem Motivation7 min
Gaussian Distribution10 min
Algorithm12 min
Building an Anomaly Detection System
Developing and Evaluating an Anomaly Detection System13 min
Anomaly Detection vs. Supervised Learning7 min
Choosing What Features to Use12 min
Multivariate Gaussian Distribution (Optional)
Multivariate Gaussian Distribution13 min
Anomaly Detection using the Multivariate Gaussian Distribution14 min
Review
Lecture Slides 10 min
https://www.coursera.org/learn/machine-learning/supplement/pB0Jq/lecture-slides
Quiz: Anomaly Detection5 questions
Recommender Systems
Predicting Movie Ratings
Problem Formulation7 min
Content Based Recommendations14 min
Collaborative Filtering
Collaborative Filtering10 min
Collaborative Filtering Algorithm8 min
Low Rank Matrix Factorization
Vectorization: Low Rank Matrix Factorization8 min
Implementational Detail: Mean Normalization8 min
Review
Lecture Slides10 min
The course has ended. Assignments may not be resubmitted.
Quiz: Recommender Systems5 questions
Programming Assignment: Anomaly Detection and Recommender Systems 3h
Week 10
Large Scale Machine Learning
Gradient Descent with Large Datasets
Learning With Large Datasets5 min
Stochastic Gradient Descent13 min
Mini-Batch Gradient Descent6 min
Stochastic Gradient Descent Convergence11 min
Advanced Topics
Online Learning12 min
Map Reduce and Data Parallelism14 min
Review
Lecture Slides 10 min
Quiz: Large Scale Machine Learning5 questions
Week 11
Application Example: Photo OCR
Photo OCR
Problem Description and Pipeline7 min
Sliding Windows14 min
Getting Lots of Data and Artificial Data16 min
Ceiling Analysis: What Part of the Pipeline to Work on Next13 min
Review
Lecture Slides 10 min
https://www.coursera.org/learn/machine-learning/supplement/Q32e6/lecture-slides