###Syllabus
The following is a tentative syllabus for the class:
- Introduction to Machine Learning. Univariate linear regression. (Optional: Linear algebra review.)
- Multivariate linear regression. Practical aspects of implementation. Octave tutorial.
- Logistic regression, One-vs-all classification, Regularization.
- Neural Networks.
- Practical advice for applying learning algorithms: How to develop, debugging, feature/model design, setting up experiment structure.
- Support Vector Machines (SVMs) and the intuition behind them.
- Unsupervised learning: clustering and dimensionality reduction.
- Anomaly detection.
- Recommender systems.
- Large-scale machine learning. An example of an application of machine learning.
Homework: (implementation model or some key parts of the model, I have finished all these homeworks with 100% points)
- IV. Linear Regression with Multiple Variables (Week 2)
- VII. Regularization (Week 3)
- VIII. Neural Networks: Representation (Week 4)
- IX. Neural Networks: Learning (Week 5)
- X. Advice for Applying Machine Learning (Week 6)
- XII. Support Vector Machines (Week 7)
- XIV. Dimensionality Reduction (Week 8)
- XVI. Recommender Systems (Week 9)
reading data, writing functions, making informative graphs, and applying modern statistical methods.