Stefan's code for Andrew Ng's Machine Learning class @ Stanford.
UPDATE: Sorry, I am not allowed to release any code.
To anyone who is currently doing the class or planning to do it:
"Do not look at any source code written by others, nor show your solution code to other students!" (Andrew Ng)
- I. Introduction 4 / 5
- II. Linear Regression with One Variable 3.2 / 5
- III. Linear Algebra Review 4 / 5
- IV. Linear Regression with Multiple Variables 5 / 5
- V. Octave Tutorial 5 / 5
- VI. Logistic Regression 5 / 5
- VII. Regularization 5 / 5
- VIII. Neural Networks: Representation 5 / 5
- IX. Neural Networks: Learning 5 / 5
- X. Advice for Applying Machine Learning 4.75 / 5
- XI. Machine Learning System Design 4.25 / 5
- XII. Support Vector Machines 5 / 5
- XIII. Clustering 4 / 5
- XIV. Dimensionality Reduction 4 / 5
- XV. Anomaly Detection 5 / 5
- XVI. Recommender Systems 5 / 5
- XVII. Large Scale Machine Learning 5 / 5
- XVIII. Application Example: Photo OCR 5 / 5
- IV. Linear Regression with Multiple Variables 150 / 100
- VII. Regularization 100 / 100
- VIII. Neural Networks: Representation 100 / 100
- IX. Neural Networks: Learning 100 / 100
- X. Advice for Applying Machine Learning 100 / 100
- XII. Support Vector Machines 100 / 100
- XIV. Dimensionality Reduction 80 / 100
- XVI. Recommender Systems 100 / 100
- Review Questions: 76 / 80 = 95%
- Programming Exercises: 700 / 700 = 100%
- Final Score: 98%
"At the end of the course, your two lowest review question scores will be dropped, along with your one lowest programming exercise score. Your total score on the programming exercises and review questions will be separately summed up and scaled to 100. The review questions make up 1/3 of your final score, and the programming exercises make up 2/3 of your final score.
To receive a statement of accomplishment, your final score must be at least 80% of the maximum possible score."