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

時間 2019-11-20

標籤 MATLAB machine learning coursera courses outline schedule 欄目 MATLAB 简体版

原文原文鏈接

課程涉及技術：

梯度降低、線性迴歸、監督/非監督學習、分類/邏輯迴歸、正則化、神經網絡、梯度檢驗/數值計算、模型選擇/診斷、學習曲線、評估度量、SVM、K-Means聚類、PCA、基於內容的推薦/方法、協同過濾、隨機梯度降低、在線學習、Map Reduce & Data Parallelism、滑動窗口、上限分析等…git

課程涉及應用：

郵件分類、腫瘤診斷、手寫識別、自動駕駛、模型優化、圖像壓縮、人臉識別、異常檢測、大數據處理、預估點擊率CTR、搜索反饋、新聞推送、文字區域檢測、字符分割、OCR、行人檢測、人工數據合成等…github

PS. 這是我上的第一門在線課程，卻也是聽過最精彩的課程之一。另外Andrew Ng 是個很是好的老師，有機會必定要去聽下這門課哦web

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

如下爲Coursera中的機器學習相關課程材料，包括練習題與個人Matlab解答.app

Github resources (Problems & Solutions):

https://github.com/Blz-Galaxy/Machine-Learning機器學習

Coursera machine learning course materials:

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

Text book:

Bayesian Reasoning and Machine Learning:

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

Video lectures:

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

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優化
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.