Skip to content

Latest commit

 

History

History
166 lines (137 loc) · 4.62 KB

README.md

File metadata and controls

166 lines (137 loc) · 4.62 KB
Raw

machine-learning-coursera

Coursera machine learning course resources.

Text book:

Bayesian Reasoning and Machine Learning http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf

Video lectures:

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

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: 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

###Final Grade: 100%

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.