Introduction to the course

Introduction to the course > Course Introduction > Intro Video

• We are just about to dive into course 2 about algorithms and machine learning.
• You learned about data collection, analysis and inference.
• You learned about data classification, linear regression, Bayesian modeling, and inference for forecasting, and even how to create compelling visualizations.
• In this course, we will talk about algorithmic techniques, including sorting, searching, [? greedy ?] algorithms, and dynamic programming, along with machine learning and how it uses algorithms to search for patterns in data.
• First of all, let us clarify how machine learning relates to statistics and data analysis.
• There is a decade-long question about the connection between the two fields, and whether machine learning and statistics should be separate fields or should merge intimately.
• In statistics, we talk about models, in machine learning about learning models.
• Machine learning cares about computational modeling and high-dimensional data.
• Week 3 we conclude the principles of algorithms, along with a case study on personal genomics presented by Professor [? Itsik ?] In week 4, you will learn about the principles of machine learning from Professor [? Peter ?] and a case study presented by Professor David Blei on probabilistic topic modeling.
• Finally, in week 5, Professor [? Orbanz ?] will go deeper into the methods of machining, and I will conclude the course with the machine learning application, to the prediction of preterm birth.