The course covers technology:
Gradient descent, linear regression, supervised/unsupervised learning, classification/logistic regression, regularization, neural network, gradient test/numerical calculation, model selection/diagnosis, learning curve, evaluation metric, SVM, K-means clustering, PCA, Map Reduce & Data Parallelism, etc...
The course covers applications:
Message classification, tumor diagnosis, handwriting recognition, autonomous driving, model optimization, OCR, etc...
Coursera machine Learning course materials, including problem sets and my solutions (using MATLAB).
The following are the machine learning related course materials in Coursera, including exercises and my MATLAB solution.
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 SubmissionScore100 / 100 points earned PASSEDSubmitted on 6 七月 2015 在 7:35 晚上Part Name Score1 Warm up exercise 10 / 102 Compute cost for one variable 40 / 403 Gradient descent for one variable 50 / 504 Feature normalization 0 / 05 Compute cost for multiple variables 0 / 06 Gradient descent for multiple variables 0 / 07 Normal equations 0 / 0
Week 3-due 07/18:
DONE
- Logistic regression
- Regularization
Programming Exercise 2:logistic Regression
Best and Most Recent SubmissionScore100 / 100 points earned PASSEDSubmitted on 8 七月 2015 在 1:00 凌晨Part Name Score1 Sigmoid function 5 / 52 Compute cost for logistic regression 30 / 303 Gradient for logistic regression 30 / 304 Predict function 5 / 55 Compute cost for regularized LR 15 / 156 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 SubmissionScore100 / 100 points earned PASSEDSubmitted on 9 七月 2015 在 1:16 凌晨Part Name Score1 Regularized logistic regression 30 / 302 One-vs-all classifier training 20 / 203 One-vs-all classifier prediction 20 / 204 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 SubmissionScore100 / 100 points earned PASSEDSubmitted on 9 七月 2015 在 7:25 晚上Part Name Score1 Feedforward and cost function 30 / 302 Regularized cost function 15 / 153 Sigmoid gradient 5 / 54 Neural net gradient function (backpropagation) 40 / 405 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 SubmissionScore100 / 100 points earned PASSEDSubmitted on 11 七月 2015 在 3:28 凌晨Part Name Score1 Regularized linear regression cost function 25 / 252 Regularized linear regression gradient 25 / 253 Learning curve 20 / 204 Polynomial feature mapping 10 / 105 Cross validation curve 20 / 20
Week 7-due 08/15:
- Support Vector Machines
- Programming Exercise 6:support Vector machines
Week 8-due 08/22:
- Clustering
- dimensionality reduction
- Programming Exercise 7:k-means Clustering and Principal Component analysis
Week 9-due 08/29:
- Anomaly Detection
- Recommender Systems
- Programming Exercise 8:anomaly Detection and Recommender Systems
Week 10/11-due 09/05:
- Large Scale Machine Learning
- Application Example:photo OCR
"MATLAB" machine learning (Coursera Courses Outline & Schedule)