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

原作者: [db:作者] 来自: [db:来源] 收藏 邀请

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


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

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 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


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: 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


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


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

   


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.


           


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