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  • 【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眨眼


    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


    Text book:

    Bayesian Reasoning and Machine Learning:

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


    Video lectures:

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


    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
    untitleduntitled1untitled2untitled3

    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
    untitled1untitled2untitled3untitled4

    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
    untitleduntitled2untitled3

    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
    
    untitleduntitled2

    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
    
    untitleduntitled2untitled3untitled4untitled5untitled6

    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

    untitleduntitled1untitled3untitled4untitled5untitled6


    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

    untitleduntitled1untitled2untitled3untitled4untitled5untitled6untitled7untitled8untitled9untitled10


    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

    untitleduntitled1untitled2untitled3

        untitled4


    Week 10/11 - Due 09/05: DONE

    • Large scale machine learning
    • Application example: Photo OCR

    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.


    PK@BX7~%LV%0_XT59XPL@QP[9]

    thx            ic-congratulations

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