About ML101

Course description

Goal

This course covers the fundamentals of the field, including supervised and unsupervised learning algorithms, regression, classification, and clustering. The course may also cover topics such as model evaluation, feature selection, and regularization.

In a supervised learning setting, students learn about linear regression and logistic regression, as well as more complex algorithms such as Naive Bayes, decision trees, random forests, and kNN. They learn how to train models on a labeled dataset and make predictions on new data.

In an unsupervised learning setting, students learn about clustering algorithms such as k-means and Apriori. They learn how to extract meaningful structure from unlabeled data.

The course may also cover advanced topics such as natural language processing. Students learn how to implement and use these algorithms in R.

Throughout the course, students work on practical projects and assignments to apply the concepts they have learned. By the end of the course, students should have a solid understanding of the basics of machine learning and be able to apply these concepts to real-world problems.

Weekly Design

2023_ML101
Week Thu Tue Pre-class Discussion Class IC-PBL
1 03/02/2023 03/07/2023 Install R & R Studio Course intro
2 03/09/2023 03/14/223 About ML & Modelling D1 Practice
3 03/16/2023 03/21/2023

Classification

  • Decision Tree
 D2 Practice Problem description
4 03/23/2023 03/28/2023
  • Random Forest
 D3 Practice Data introduction
5 03/30/2023 04/04/2023
  • Naive Bayes
 D4 Practice Team arrangement
6 04/06/2023 04/11/2023
  • kNN
 D5 Practice Team meeting #1
7 04/13/2023 04/18/2023

Regression

  • Linear regression
 D6 Practice Team meeting #2
8 QZ #1: 04/19 (Wed)
13:00 ~ 15:00		 Team meeting #3
9 04/27/2023 05/02/2023
  • Non-linear regression
 D7 Practice Team meeting #4
10 05/04/2023 05/09/2023

Unsupervised learning

  • Clustering
 D8 Practice Team meeting #5
11 05/11/2023 05/16/2023
  • Apriori
 D9 Practice Team meeting #6
12 05/18/2023 05/23/2023 Model improvement D10 Practice Team meeting #7
13 05/25/2023 05/30/2023 Text mining & other skills Practice Team consulting #1
14 QZ #2: 05/31 (Wed)
13:00 ~ 15:00		 Team consulting #2
15 최종 프로젝트 발표일:
6월 14일(수)		 Project Presentation

Rules

  • No score for Attendance (Come to class if you want to learn something)

  • Pre-class: 100% Eng

  • Class (Practice part): 50% Eng / 50% Kor

  • Discussion Submission: 100% Eng.

  • QZ #1 & #2 : 100% Eng.

  • IC-PBL project (100% Kor.)

    • Only for Domestic Students because the company that gives the problem for the class cannot proceed the project in English.

    • ICPBL 프로젝트는 한국 학생들로만 팀을 구성하여 진행

    • Exchange Students: I’ll give a different (virtual) problem set for you guys.


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