标签：

课程涉及技术：

梯度下降、线性回归、监督/非监督学习、分类/逻辑回归、正则化、神经网络、梯度检验/数值计算、模型选择/诊断、学习曲线、评估度量、SVM、K-Means聚类、PCA、Map Reduce & Data Parallelism 等…

课程涉及应用：

邮件分类、肿瘤诊断、手写识别、自动驾驶、模型优化、OCR等…

Coursera machine learning course materials, including problem sets and my solutions (using matlab).

以下为Coursera中的机器学习相关课程材料，包括练习题与我的Matlab解答.

Github resources (Problems & Solutions):

https://github.com/Blz-Galaxy/Machine-Learning

Coursera machine learning course materials:

https://class.coursera.org/ml/lecture/preview

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Text book:

Bayesian Reasoning and Machine Learning:

http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf

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Video lectures:

https://www.coursera.org/learn/machine-learning

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Schedule:

Week 1 - Due 07/04: DONE

• Introduction
• Linear regression with one variable
• Linear Algebra review (Optional)

Week 2 - Due 07/11: DONE

• Linear regression with multiple variables
• Octave tutorial

• Programming Exercise 1: Linear Regression

  Best and Most Recent Submission
  Score
  100 / 100 points earned PASSED
  Submitted on 6 七月 2015 在 7:35 晚上
  Part    Name    Score
  1   Warm up exercise    10 / 10
  2   Compute cost for one variable   40 / 40
  3   Gradient descent for one variable   50 / 50
  4   Feature normalization   0 / 0
  5   Compute cost for multiple variables 0 / 0
  6   Gradient descent for multiple variables 0 / 0
  7   Normal equations    0 / 0

Week 3 - Due 07/18: DONE

• Logistic regression
• Regularization

• Programming Exercise 2: Logistic Regression

  Best and Most Recent Submission
  Score
  100 / 100 points earned PASSED
  Submitted on 8 七月 2015 在 1:00 凌晨
  Part    Name    Score
  1   Sigmoid function    5 / 5
  2   Compute cost for logistic regression    30 / 30
  3   Gradient for logistic regression    30 / 30
  4   Predict function    5 / 5
  5   Compute cost for regularized LR 15 / 15
  6   Gradient for regularized LR 15 / 15

Week 4 - Due 07/25: DONE

• Neural Networks: Representation

• Programming Exercise 3: Multi-class Classification and Neural Networks

  Best and Most Recent Submission
  Score
  100 / 100 points earned PASSED
  Submitted on 9 七月 2015 在 1:16 凌晨
  Part    Name    Score
  1   Regularized logistic regression 30 / 30
  2   One-vs-all classifier training  20 / 20
  3   One-vs-all classifier prediction    20 / 20
  4   Neural network prediction function  30 / 30
  

Week 5 - Due 08/01: DONE

• Neural Networks: Learning

• Programming Exercise 4: Neural Networks Learning

  Best and Most Recent Submission
  Score
  100 / 100 points earned PASSED
  Submitted on 9 七月 2015 在 7:25 晚上
  Part    Name    Score
  1   Feedforward and cost function   30 / 30
  2   Regularized cost function   15 / 15
  3   Sigmoid gradient    5 / 5
  4   Neural net gradient function (backpropagation)  40 / 40
  5   Regularized gradient    10 / 10
  

Week 6 - Due 08/08: DONE

• Advice for applying machine learning
• Machine learning system design

• Programming Exercise 5: Regularized Linear Regression and Bias v.s. Variance

  Best and Most Recent Submission
  Score
  100 / 100 points earned PASSED
  Submitted on 11 七月 2015 在 3:28 凌晨
  Part    Name    Score
  1   Regularized linear regression cost function 25 / 25
  2   Regularized linear regression gradient  25 / 25
  3   Learning curve  20 / 20
  4   Polynomial feature mapping  10 / 10
  5   Cross validation curve  20 / 20
  

Week 7 - Due 08/15:

• Support vector machines
• Programming Exercise 6: Support Vector Machines

Week 8 - Due 08/22:

• Clustering
• Dimensionality reduction
• Programming Exercise 7: K-means Clustering and Principal Component Analysis

Week 9 - Due 08/29:

• Anomaly Detection
• Recommender Systems
• Programming Exercise 8: Anomaly Detection and Recommender Systems

Week 10/11 - Due 09/05:

• Large scale machine learning
• Application example: Photo OCR

【MATLAB】Machine Learning (Coursera Courses Outline & Schedule)

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原文地址：http://www.cnblogs.com/KC-Mei/p/4637876.html