(1) Students understand the fundamental concepts and off-the-shelf methods in modern statistical machine learning.
(2) Students are equipped with basic skills of contemporary machine learning tools and libraries.
(3) Students learn how to approach real-world data analysis.

Course coverage: (1) Gentle introduction to machine learning
(2) Introduction to Python
(3) Methods for learning from data: k-nearest neighbors, decision trees, and logistic regression
(4) Improving model performance using the ensemble techniques
(5) Introduction to deep learning
(6) Model and data visualization
(7) Real-world data analysis

(S/U) grade based on the class participation (40%) and the report assignment (60%).

There is no official textbook. PDF files of lecture notes and hands-on activities will be distributed.

Year 2022 11st July – 15th July (Monday-Friday)
Lecture schedule: 1:00 pm – 5:00 am on Monday-Friday.
Instructor: Professor Charmgil Hong (홍참길), Email: charmgil@handong.edu
School of Computer Science and Electrical Engineering, Handong Global University,

Session 1: Introduction (2 hr)
- Gentle introduction to machine learning
- Introduction to Python
Session 2: Classification with k-nearest neighbors (2 hr)
Session 3: Prediction with decision trees (2 hr)
Session 4: Visualization using Matplotlib (2 hr)
Session 5: Classification with logistic regression (2 hr)
Session 6: Ensemble learning (2 hr)
Session 7: Introduction to deep learning (2 hr)
Session 8: Image classification with convolutional neural networks (2 hr)
Session 9: Brief introduction to outlier detection (4 hr)