Applied Statistics and Data Analysis

• Textbook & References: 

  • 1. Probability and Statistics for Computer Science
    by David Forsyth.
    Springer International Publishing, 2018.
    [https://link.springer.com/book/10.1007/978-3-319-64410-3] 

  • 2. An Introduction to Statistical Learning
    by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, & Jonathan Taylor,
    Springer International Publishing, 2023.
    [https://link.springer.com/book/10.1007/978-3-031-38747-0]
    Also,
    [https://www.statlearning.com/]

  • 3. Foundations of Data Science
    by Avrim Blum, John Hopcroft, and Ravindran Kannan.
    2020 version is accessible via the first author’s website:
    [https://home.ttic.edu/~avrim/book.pdf]

• Main Topics:

  •  Statistics for Computer Science:
    descriptive statistics, sampling methods, populations of data, significance of evidence, probability models from data. 

  • Statistical learning models:
    estimate vs. prediction, maximum likelihood method, bootstrap, regularization, maximum entropy method, ...... 

  • Foundations of (Massive and High-dimensional) Data Science:
    essential properties of high-dimensional space, best-fit subspaces, singular value decomposition, algorithms for massive data

• Evaluation:

Class participation15%, Homework & Project 50%,
Midterm and Final Exam 35%