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

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、基于内容的推荐/方法、协同过滤、随机梯度下降、在线学习、Map Reduce & Data Parallelism、滑动窗口、上限分析等…

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、图像压缩、人脸识别、异常检测、大数据处理、预估点击率CTR、搜索反馈、新闻推送、文字区域检测、字符分割、OCR、行人检测、人工数据合成等…

PS. 这是我上的第一门在线课程，却也是听过最精彩的课程之一。另外Andrew Ng 是个非常好的老师，有机会一定要去听下这门课哦

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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)

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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

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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

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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

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Week 5 - Due 08/01: DONE

• Neural Networks: Learning

• Programming Exercise 4: Neural Networks Learning

  Best and Most Recent Submission
  Score
  100 / 100 points earnedPASSED
  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

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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

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Week 7 - Due 08/15: DONE

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

  Best and Most Recent Submission
  Score
  100 / 100 points earned PASSED
  Submitted on 12 七月 2015 在 2:48 凌晨
  Part    Name    Score
  1    Gaussian kernel    25 / 25
  2    Parameters (C, sigma) for dataset 3    25 / 25
  3    Email preprocessing    25 / 25
  4    Email feature extraction    25 / 25

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Week 8 - Due 08/22: DONE

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

  Best and Most Recent Submission
  Score
  100 / 100 points earned PASSED
  Submitted on 13 七月 2015 在 2:45 凌晨
  Part    Name    Score
  1    Find closest centroids    30 / 30
  2    Compute centroid means    30 / 30
  3    PCA    20 / 20
  4    Project data    10 / 10
  5    Recover data    10 / 10

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Week 9 - Due 08/29: DONE

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

  Best and Most Recent Submission
  Score
  100 / 100 points earned PASSED
  Submitted on 14 七月 2015 在 8:12 晚上
  Part    Name    Score
  1    Estimate gaussian parameters    15 / 15
  2    Select threshold    15 / 15
  3    Collaborative filtering cost    20 / 20
  4    Collaborative filtering gradient    30 / 30
  5    Regularized cost    10 / 10
  6    Gradient with regularization    10 / 10

   

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Week 10/11 - Due 09/05: DONE

• Large scale machine learning
• Application example: Photo OCR

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Summary

• Supervised Learning

        Linear regression, logistic regression, neural networks, SVMs

• Unsupervised Learning

        K-means, PCA, Anomaly detection

• Special applications/special topics

        Recommender systems, large scale machine learning

• Advice on building a machine learning system

        Bias/variance, regularization; deciding what to work on next: evalution of learning algorithms, learning curves, error analysis, ceiling analysis.

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