Reading 14: Classification I: Introduction, Generative Classification

Reading 14: Classification I: Introduction, Generative Classification#

For the class on Monday, March 25th

Reading assignments#

  1. Read the following sections of [ICVG20]:

    • Chap. 9 “Classification”

      • (Optional) Sec. 9.1 “Data Sets Used in This Chapter

      • Sec. 9.2 “Assigning Categories: Classification”

        • Sec. 9.2.1 “Classification Loss”

      • Sec. 9.3 “Generative Classification”

        • Sec. 9.3.1 “General Concepts of Generative Classification”

        • Sec. 9.3.2 “Naive Bayes”

        • (Optional) Sec. 9.3.3–9.3.5

      • Sec. 9.4 “K-Nearest-Neighbor Classifier”



Submit your answer on Canvas. Due at noon, Monday, March 25th.

  1. List anything from your reading that confuses you. Explain why they confuse you. You are strongly encouraged to think about what questions you have about the reading, but if you really have no questions at all, please briefly summarize what you have learned from this reading assignment.

  2. Use your own words to explain the difference between a “generative” classification method and a “discriminative” classification method.

  3. What quantity does a classification method aim to minimize?

Discussion Preview#


We will discuss the following questions in class. They are included here so that you have a chance to think about them before class. You need not submit your answers as part of this assignment.

  1. We will discuss the overall mathematical framework of classification methods and the relationship between the generative methods and density estimation.

  2. We will compare a few different classification methods (and will focus on generative methods in this class).