This course introduces basic concepts and algorithms in machine learning.
Topics covered include:
- Data Modeling Strategies
- Probability Theory
- Mixture Models
- Linear Methods and Models
- Tree Models
- Graphical Models
- Support Vector Machines
- Model Selection
- Sampling
- Unsupervised Learning
- Neural Networks
- Deep Learning
- Reinforcement Learning
- Large Scale Machine Learning
- Industry Guide Lines