| Academic Year |
2025Year |
School/Graduate School |
Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program |
| Lecture Code |
WSN20901 |
Subject Classification |
Specialized Education |
| Subject Name |
Artificial and Natural Intelligence |
Subject Name
（Katakana） |
アーティフィカル アンド ナチュラル インテリジェンス |
Subject Name in
English |
Artificial and Natural Intelligence |
| Instructor |
RAYTCHEV BISSER ROUMENOV |
Instructor
(Katakana) |
ライチェフ ビセル ルメノフ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, First Semester, 2Term |
| Days, Periods, and Classrooms |
(2T) Mon5-8：ENG 105 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face |
| lectures, labs, presentations and programming assignments (might shift to online depending on conditions, details will be given during the guidance) |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
E
:
English |
| Course Level |
6
:
Graduate Advanced
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
Artificial Intelligence (AI), Deep Learning, Machine learning, PyTorch |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Class Objectives
/Class Outline |
Each student will be allocated material from the book related to deep learning and will have to prepare the relevant material and make a presentation. Also, some programming projects might be assigned. |
| Class Schedule |
1 Guidance
2 Introduction to Deep Learning
3 Basics of PyTorch
4 Linear Neural Networks
5 Multilayer Perceptrons
6 Layers and Modules
7 Convolutional Neural Networks
8 Modern Convolutional Neural Networks
9 Recurrent Neural Networks
10 Modern Recurrent Neural Networks
11 Attention Mechanisms
12 Optimization Algorithms
13 Optimization Algorithms
14 Computational Performance
15 Final exam (paper test)
Presentation, programming assignment, final exam. |
Text/Reference
Books,etc. |
Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, Dive into Deep Learning, 2023.
|
PC or AV used in
Class,etc. |
Text, Handouts, Visual Materials, Microsoft Teams, moodle |
| (More Details) |
PC, PowerPoint slides, Jupyter notebooks |
| Learning techniques to be incorporated |
Discussions, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning, Flip Teaching |
Suggestions on
Preparation and
Review |
It is important to complete the programming assignments and read assigned chapters. Previous programming experience with Python is assumed (details will be explained during the guidance). |
| Requirements |
PyTorch will be used for the lab/programming assignments (for that reason it is necessary to have some previous experience with Python). |
| Grading Method |
Evaluation based on comprehensive assessment of the programming assignments, presentation and final test. |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |