First Programming – Python for AI – step2

(1) Purpose and content of the course

This is a lecture-style course for those who are new to programming and AI. Based on the content of “step 1” of the course of the same name, participants will learn how to write Python in a step-by-step manner. After learning about classes, methods, instances, and inheritance, which are necessary for implementing machine learning, etc., participants will learn about data structures and data processing while touching on useful Python libraries. After attending this lecture, students will next attend “Introduction to Machine Learning” to learn how to handle AI using Python. It is also recommended to take this course in combination with “Programming for the First Time – Python for AI – step2 (Exercises),” which focuses on exercises on the lecture content.

(2) Contents of each session

Session 1: “Introduction to the Class

Summarize the review of step1 and learn about classes. First, the contents and schedule of this course will be explained. As a supplement, the environment construction will be explained to those who are taking the course from this course. Then, an overview of the knowledge learned in step1 and the knowledge that will be acquired in the future will be given. Next, the concept and importance of classes will be explained, and the usage of classes will be taught. In addition, students will learn about creating instances, member variables and member functions, and the difference between classes and instances. In addition, time will be set aside for students to solve exercises to deepen their understanding of the lecture.

Session 2: “Class Development

Students will learn how to use classes in a more advanced way. First, review the usage of classes and instances in the first session. Then, constructors and info methods will be explained and their usage will be studied. Next, the concept of inheritance will be explained and its concrete usage will be studied. In addition, other advanced topics such as modifiers will be covered. Finally, students will review the classes learned in the first and second sessions to consolidate their knowledge. In addition, students will deepen their understanding of the lecture through exercises.

Session 3: “Library Part 1

Students will learn how to use libraries, especially NumPy. First, review the classes in the first two sessions. Next, the concepts of libraries will be explained and their usage will be studied. Specifically, the usage of import statement will be explained and math module will be introduced as an example. Then, the use and importance of the NumPy library, the difference between lists and NumPy arrays will be explained, and how to create one-dimensional and multi-dimensional arrays and their calculations, manipulate array elements, and compare speeds. In addition, exercises will deepen the understanding of the lecture.

Session 4: “Libraries Part 2”

Learn how to use libraries, especially Pandas and Matplotlib. First, review NumPy from Session 3. Next, the usage and importance of the Pandas library will be explained, and tabular data will be manipulated using DataFrame. Then, the use and importance of the Matplotlib library will be explained, and data will be visualized in graphs and other formats. Furthermore, the lecture will explain how to read CSV format data and perform statistical processing using actual data sets. In addition, students will deepen their understanding of the lecture through exercises.

Session 5: “Python Completion

Using the Python knowledge acquired through step1 and step2, students will learn about Sympy, an algebraic computation library, and Scipy, a numerical computation library, which are useful for research and scientific calculations. In Sympy, the algebraic computation library, students will learn how to transform expressions, perform differential and integral calculations, derive solutions to equations, and compute linear algebra. In Scipy, a numerical library, students learn how to derive optimal solutions, and how to check the results of optimization through a large number of computer calculations using 3-dimensional mapping and color maps.