1.Why (purpose)

The goal is to quickly establish a decision cooperative system with data.:

Introduce Weka, which can both design and program Java machine learning.

2. What (Lectures)

Here's a real machine learning application with weka alone

: we've adapted a variety of application cases into familiar content.

Then shall we briefly introduce the contents?

2.1 Adopting an Optimal Algorithm by the Experimenter

: Adopting an Optimal Model through a P-value Statistical Test (where did you hear that?)

2.2 Making decisions with the feature selection

: creating decision information with a specific (attribute) selection. R program linkage is a bonus.

2.3 Survey Text Mining

: No more wrestling with the difficult Hangul Formosa! A simple Korean survey can be done with basic functions.

2.4 Correlation and Classification of U.S. House Elections in 84

: The Obama camp did not anticipate election pledges, but chose statistical analysis of its website to raise campaign funds: What's really important is to know which pledges are directly linked to election?

2.5 Image Analysis by Artificial Neural Network and Image Filter

: Tired of waiting for a beta version of dl4j: Introducing Weka's built-in Artificial Neural Network and wekadeepleasing4j.

2.6 Estimate when a course is completed through regression analysis

: Used to determine how long a lecture should be postponed.

3. Method

The above process is explained in three order as follows.

3.1 Theoretical Description

: Background knowledge is brief. It's really simple, it's all about the point.

3.2 KnowledgeFlow Design

: Weka's Best Advantage - Machine learning is possible without programming.

3.3 Java programming

: Another advantage of weka, weka provides everything for design and coding.

4. IF (effectiveness)

You can apply loaded data analysis to traditional IT systems that consist of Java platforms

: Would you like to know how to take a post way in traditional IT so that you can analyze it well on ICBMs?

You get a way to understand the real world with your data

: to understand the unseen reality with your data.

5. Beginner's reflection point → Supplementary intermediate course

It's an intermediate course that has improved more than a beginner's class for the base change of Java machine learning.:

Through feedback and self-reflection from the students on the beginner's course, we have improved more than the beginner's course.

6. Lecture environment

Use weka 3.9.3 for Windows OS

(3.9.4 has a bug when importing ANSI type files).

7. Lecture materials

: You can download it by clicking the cloud icon in the second class in section 1

(installing Weka software and downloading course materials). (55 MB)

8. Continuation of Extra lecture

I will continue to upload lecture from the students' good questions and useful information from other media.

I look forward to strengthening communication with my students and improving their satisfaction with lectures.

Exclude hard, requirements, or personal questions.

9. Caution

※ You can see Korean in the video or lecture materials.

※ But you don't have to worry that you don't understand Korean.

※ The Korean language you see in the lecture can be considered to only non-utf-8 data.