Course Details
| Title | Introduction to AI and Deep Learning Basics |
| Field of Study | Engineering |
| Professor | Youngjoon Won (youngjoon@hanyang.ac.kr) |
| Type | Academic course |
| Delivery Type | Hybrid Track (2 weeks online + 2 weeks offline): Real-time |
| Credits | 3 |
| Contact hours | 45 |
| Schedule | Morning |
| Course code | ISS1158 |
| Course number | 18056 |
| Description |
This course provides an introduction to the fundamental problems of artificial |
| Objective |
This course is designed to introduce the field of artificial intelligence and basic |
| Preparations |
Should be able to write and understand texts in English. Materials: All materials are provided in class. Appropriate urls, tech-blogs, sample codes will be |
| Materials |
|
| Evaluation |
Assignment
20%
Attendance
10%
Group Project
40%
Midterm
20%
Presentation
10% |
| Lesson Plan |
Class 1:
Introduction to AI and agents, examples: A quick look at Titanic survival prediction
Class 2:
Understanding states and dimensions of models: Pyrobot vacuum cleaner
Class 3:
Generic searching problems: 8-puzzle example
Class 4:
Overview of ML, Supervised learning
Class 5:
In-class presentation by group, selected publications from KDD
Class 6:
Overview of ML, Supervised learning: Decision tree algorithms, entropy
Class 7:
Overview of ML, Unsupervised learning: K-mean clustering
Class 8:
Overview of ML, Feature extraction: Mushroom classification problem, Brightics
Class 9:
Deep learning basics: Introduction to neural networks (NN)
Class 10:
Deep learning basics: Gradient descent and model training/test
Class 11:
Keras/Tensorflow examples: Student admission prediction
Class 12:
Introduction to CNN
Class 13:
AWS Sagemaker exercise (ground truth): Sound classification problem (tentative for now)
Class 14:
Introduction to constrain satisfaction problem (CSP)
Class 15:
Take Home Exam or Group Project Presentation
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| Last Updated | April 15, 2021 |