Coursera machine learning course resources.

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

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

• Introduction
• Linear regression with one variable
• Linear Algebra review (Optional)

• 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
  

• 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
  

• 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
  

• 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
  

• 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
  

• 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
  

• 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
  

• 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
  

• Large scale machine learning
• Application example: Photo OCR

###Final Grade: 100%

-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.