Introduction to the course

Introduction to the course

“introduction to the course”
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Summaries

  • Introduction to the course > Course Introduction > Intro Video

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.

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