This course introduces techniques in machine learning with an emphasis on algorithms and their applications to real-world data. We will investigate the following question: how to computationally extract useful knowledge from data for decision making and task support! The course will also cover briefly Ethics in Machine Learning and Secure Computing. We will focus on machine learning methods, which are organized into three parts:
- Basic math for data science and machine learning
- Linear algebra
- Probability and statistics
- Information theory
- Optimization
- Unsupervised machine learning for data exploration
- Clustering analysis
- Dimensionality reduction
- Kernel density estimation
- Supervised learning for predictive data analysis
- Tree-based models
- Support vector machines
- Linear classification and regression
- Neural networks
- Structuring a task into a machine learning work flow
- Collaborating effectively on team projects in a remote environment
- Conducting peer evaluation in a constructive format
- Communicating technical content in a concise and effective manner