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Machine-Learning

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Study Machine Learning in the Coursera

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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.

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