This course serves as an overview of a comprehensive field comprising 15+ courses. It is not a prerequisite for any other course, nor is any other course a prerequisite for it, except for basic knowledge of calculus and probability at the high school or elementary college level. The primary goal of this course is to motivate students to pursue further studies in the field and excel in each course.
The course begins by covering the fundamentals of probability theory, statistics, linear algebra, and data visualization. It then progresses to introduce the basics of Statistical Decision Theory, followed by an exploration of Linear Models and Regression. The course will derive and explain Bayes' classifier, along with its application to multinormal distributions. This will lead to the definition of various statistical concepts such as estimation, loss function, and risk minimization.
Since each of these topics is extensively covered in standalone courses within the field (such as probability, statistics, and pattern recognition), this course will focus on practical applications rather than theoretical aspects. Mathematical derivations and heavy mathematical treatment will not be extensively covered. Emphasis will be placed on developing intuition and solving computer problems using Python.
