SEMANA 1

Introduction

Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.

5 vídeos, 9 leituras

Reading: Machine Learning Honor Code
Vídeo: Welcome
Vídeo: What is Machine Learning?
Reading: What is Machine Learning?
Reading: How to Use Discussion Forums
Vídeo: Supervised Learning
Reading: Supervised Learning
Vídeo: Unsupervised Learning
Reading: Unsupervised Learning
Reading: Who are Mentors?
Reading: Get to Know Your Classmates
Reading: Frequently Asked Questions
Reading: Lecture Slides

Nota atribuída: Introduction

Linear Regression with One Variable

Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.

7 vídeos, 8 leituras

Reading: Model Representation
Vídeo: Cost Function
Reading: Cost Function
Vídeo: Cost Function - Intuition I
Reading: Cost Function - Intuition I
Vídeo: Cost Function - Intuition II
Reading: Cost Function - Intuition II
Vídeo: Gradient Descent
Reading: Gradient Descent
Vídeo: Gradient Descent Intuition
Reading: Gradient Descent Intuition
Vídeo: Gradient Descent For Linear Regression
Reading: Gradient Descent For Linear Regression
Reading: Lecture Slides

Nota atribuída: Linear Regression with One Variable

Linear Algebra Review

This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.

6 vídeos, 7 leituras, 1 questionário de prática

Reading: Matrices and Vectors
Vídeo: Addition and Scalar Multiplication
Reading: Addition and Scalar Multiplication
Vídeo: Matrix Vector Multiplication
Reading: Matrix Vector Multiplication
Vídeo: Matrix Matrix Multiplication
Reading: Matrix Matrix Multiplication
Vídeo: Matrix Multiplication Properties
Reading: Matrix Multiplication Properties
Vídeo: Inverse and Transpose
Reading: Inverse and Transpose
Reading: Lecture Slides
Practice Quiz: Linear Algebra

SEMANA 2

Linear Regression with Multiple Variables

What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression.

8 vídeos, 16 leituras

Reading: Setting Up Your Programming Assignment Environment
Reading: Accessing MATLAB Online and Uploading the Exercise Files
Reading: Installing Octave on Windows
Reading: Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)
Reading: Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)
Reading: Installing Octave on GNU/Linux
Reading: More Octave/MATLAB resources
Reading: Multiple Features
Vídeo: Gradient Descent for Multiple Variables
Reading: Gradient Descent For Multiple Variables
Vídeo: Gradient Descent in Practice I - Feature Scaling
Reading: Gradient Descent in Practice I - Feature Scaling
Vídeo: Gradient Descent in Practice II - Learning Rate
Reading: Gradient Descent in Practice II - Learning Rate
Vídeo: Features and Polynomial Regression
Reading: Features and Polynomial Regression
Vídeo: Normal Equation
Reading: Normal Equation
Vídeo: Normal Equation Noninvertibility
Reading: Normal Equation Noninvertibility
Vídeo: Working on and Submitting Programming Assignments
Reading: Programming tips from Mentors
Reading: Lecture Slides

Nota atribuída: Linear Regression with Multiple Variables

Octave/Matlab Tutorial

This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.

6 vídeos, 1 leitura

Vídeo: Moving Data Around
Vídeo: Computing on Data
Vídeo: Plotting Data
Vídeo: Control Statements: for, while, if statement
Vídeo: Vectorization
Reading: Lecture Slides
Programming: Linear Regression

Nota atribuída: Octave/Matlab Tutorial

SEMANA 3

Logistic Regression

Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.

7 vídeos, 8 leituras

Reading: Classification
Vídeo: Hypothesis Representation
Reading: Hypothesis Representation
Vídeo: Decision Boundary
Reading: Decision Boundary
Vídeo: Cost Function
Reading: Cost Function
Vídeo: Simplified Cost Function and Gradient Descent
Reading: Simplified Cost Function and Gradient Descent
Vídeo: Advanced Optimization
Reading: Advanced Optimization
Vídeo: Multiclass Classification: One-vs-all
Reading: Multiclass Classification: One-vs-all
Reading: Lecture Slides

Nota atribuída: Logistic Regression

Regularization

Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data.

4 vídeos, 5 leituras

Reading: The Problem of Overfitting
Vídeo: Cost Function
Reading: Cost Function
Vídeo: Regularized Linear Regression
Reading: Regularized Linear Regression
Vídeo: Regularized Logistic Regression
Reading: Regularized Logistic Regression
Reading: Lecture Slides
Programming: Logistic Regression

Nota atribuída: Regularization

SEMANA 4

Neural Networks: Representation

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.

7 vídeos, 6 leituras

Vídeo: Neurons and the Brain
Vídeo: Model Representation I
Reading: Model Representation I
Vídeo: Model Representation II
Reading: Model Representation II
Vídeo: Examples and Intuitions I
Reading: Examples and Intuitions I
Vídeo: Examples and Intuitions II
Reading: Examples and Intuitions II
Vídeo: Multiclass Classification
Reading: Multiclass Classification
Reading: Lecture Slides
Programming: Multi-class Classification and Neural Networks

Nota atribuída: Neural Networks: Representation

SEMANA 5

Neural Networks: Learning

In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition.

8 vídeos, 8 leituras

Reading: Cost Function
Vídeo: Backpropagation Algorithm
Reading: Backpropagation Algorithm
Vídeo: Backpropagation Intuition
Reading: Backpropagation Intuition
Vídeo: Implementation Note: Unrolling Parameters
Reading: Implementation Note: Unrolling Parameters
Vídeo: Gradient Checking
Reading: Gradient Checking
Vídeo: Random Initialization
Reading: Random Initialization
Vídeo: Putting It Together
Reading: Putting It Together
Vídeo: Autonomous Driving
Reading: Lecture Slides
Programming: Neural Network Learning

Nota atribuída: Neural Networks: Learning

SEMANA 6

Advice for Applying Machine Learning

Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models.

7 vídeos, 7 leituras

Vídeo: Evaluating a Hypothesis
Reading: Evaluating a Hypothesis
Vídeo: Model Selection and Train/Validation/Test Sets
Reading: Model Selection and Train/Validation/Test Sets
Vídeo: Diagnosing Bias vs. Variance
Reading: Diagnosing Bias vs. Variance
Vídeo: Regularization and Bias/Variance
Reading: Regularization and Bias/Variance
Vídeo: Learning Curves
Reading: Learning Curves
Vídeo: Deciding What to Do Next Revisited
Reading: Deciding What to do Next Revisited
Reading: Lecture Slides
Programming: Regularized Linear Regression and Bias/Variance

Nota atribuída: Advice for Applying Machine Learning

Machine Learning System Design

To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.

5 vídeos, 3 leituras

Reading: Prioritizing What to Work On
Vídeo: Error Analysis
Reading: Error Analysis
Vídeo: Error Metrics for Skewed Classes
Vídeo: Trading Off Precision and Recall
Vídeo: Data For Machine Learning
Reading: Lecture Slides

Nota atribuída: Machine Learning System Design

SEMANA 7

Support Vector Machines

Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice.

6 vídeos, 1 leitura

Vídeo: Large Margin Intuition
Vídeo: Mathematics Behind Large Margin Classification
Vídeo: Kernels I
Vídeo: Kernels II
Vídeo: Using An SVM
Reading: Lecture Slides
Programming: Support Vector Machines

Nota atribuída: Support Vector Machines

SEMANA 8

Unsupervised Learning

We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points.

5 vídeos, 1 leitura

Vídeo: K-Means Algorithm
Vídeo: Optimization Objective
Vídeo: Random Initialization
Vídeo: Choosing the Number of Clusters
Reading: Lecture Slides

Nota atribuída: Unsupervised Learning

Dimensionality Reduction

In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets.

7 vídeos, 1 leitura

Vídeo: Motivation II: Visualization
Vídeo: Principal Component Analysis Problem Formulation
Vídeo: Principal Component Analysis Algorithm
Vídeo: Reconstruction from Compressed Representation
Vídeo: Choosing the Number of Principal Components
Vídeo: Advice for Applying PCA
Reading: Lecture Slides
Programming: K-Means Clustering and PCA

Nota atribuída: Principal Component Analysis

SEMANA 9

Anomaly Detection

Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.

8 vídeos, 1 leitura

Vídeo: Gaussian Distribution
Vídeo: Algorithm
Vídeo: Developing and Evaluating an Anomaly Detection System
Vídeo: Anomaly Detection vs. Supervised Learning
Vídeo: Choosing What Features to Use
Vídeo: Multivariate Gaussian Distribution
Vídeo: Anomaly Detection using the Multivariate Gaussian Distribution
Reading: Lecture Slides

Nota atribuída: Anomaly Detection

Recommender Systems

When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.

6 vídeos, 1 leitura

Vídeo: Content Based Recommendations
Vídeo: Collaborative Filtering
Vídeo: Collaborative Filtering Algorithm
Vídeo: Vectorization: Low Rank Matrix Factorization
Vídeo: Implementational Detail: Mean Normalization
Reading: Lecture Slides
Programming: Anomaly Detection and Recommender Systems

Nota atribuída: Recommender Systems

SEMANA 10

Large Scale Machine Learning

Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets.

6 vídeos, 1 leitura

Vídeo: Stochastic Gradient Descent
Vídeo: Mini-Batch Gradient Descent
Vídeo: Stochastic Gradient Descent Convergence
Vídeo: Online Learning
Vídeo: Map Reduce and Data Parallelism
Reading: Lecture Slides

Nota atribuída: Large Scale Machine Learning

SEMANA 11

Application Example: Photo OCR

Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.

5 vídeos, 1 leitura

Vídeo: Sliding Windows
Vídeo: Getting Lots of Data and Artificial Data
Vídeo: Ceiling Analysis: What Part of the Pipeline to Work on Next
Reading: Lecture Slides
Vídeo: Summary and Thank You

Nota atribuída: Application: Photo OCR

Idioma	English, Legendas: Spanish, Hindi, Japanese, Chinese (Simplified)
Como ser aprovado	Seja aprovado em todas as tarefas para concluir o curso.
Classificação do usuário	4.9 estrelas Classificação média do usuário 4.9Veja o que os aprendizes disseram

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