Course Title
Department/Course
Category/Spacializations
Year Offered
Required or Elective
Semester
Day/Period
Chief Instructor
Instructors

Artificial Intelligence and Machine Learning
Graduate School of Marine Science and Technology Master's Course<Graduate School Subjects>、<Other Courses' Subjects>
Class
1stCredit
elective
Course TypeFirst Semester
Lecture Room
WED３
Matsui Kazunori
Matsui Kazunori

Theme & Objects

The aim of this course is to understand the history and background of artificial intelligence and to learn thefundamental theories of machine learning. This includes deepening the understanding of specific algorithms throughtheoretical aspects and practical application using Python.

Learning OutcomesStudents understand and can explain the basic terminology of artificial intelligence and machine learning. They
understand what techniques such as linear regression, k-nearest neighbors, and k-means clustering, etc., are andcan select and apply these methods appropriately according to the situation.

102Combination of remote (real-time and on-demand)

Styles of Class

Course Contents

Prerequisites

Note: "Combination" includes simultaneous live streaming of in-person classes, conducting classes in-person orremotely on a per-session basis, or a mix of both approaches.
1. About Artificial Intelligence & Machine Learning
2. Theory and Implementation of Regression: Linear Regression
3. Theory and Implementation of Regression: Regularization of Linear Regression
4. Theory and Implementation of Classification: Logistic Regression
5. Theory and Implementation of Classification: Support Vector Machines
6. Theory and Implementation of Classification: k-Nearest Neighbors
7. Midterm Summary
8. Theory and Implementation of Classification: Decision Trees
9. Theory and Implementation of Clustering: k-Means
10. Theory and Implementation of Clustering: Hierarchical Clustering
11. Fundamentals of Neural Networks
12. Implementation of Neural Networks
13. Recent Topics in Neural Networks
A foundational understanding of linear algebra, calculus, and basic statistical calculations (such as matrix
multiplication, differentiation, and expectation) is required as prior knowledge.
Experience with Python is optional, but having a basic understanding beforehand is recommended to reduce theworkload for preparation and review.

Textbook / References
Preparation & Review

Prepare for classes using the lecture materials distributed in class, and review the materials and your notes.

Assessment and Examinations

Reports
The minimum criteria for passing are based on the attainment level of understanding the following:
1. Understanding the specialized terminology related to artificial intelligence and machine learning.
2. Being able to perform calculations based on machine learning methods.

Evaluation Criteria
Teaching activities & methods
Instructor Contact Information

Appointments must be made in advance via email.
kmat002＊kaiyodai.ac.jp
Please replace "＊" by "@".

Other Information
URL
CodeTeaching Language

EnglishNote: Japanese materials and explanations or question handling in Japanese will be provided as needed.

Workload Calculation
Related Degree Awarding Policy
Relation to SDGs

Select 1 to 3 applicable items and delete the non-applicable items. After selecting, plase delete this description.
4 Quality education