2022 Machine Learning Framework

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Academic unit or major
Graduate major in Systems and Control Engineering
Kitamura Koji 
Class Format
Lecture    (Livestream)
Media-enhanced courses
Day/Period(Room No.)
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
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Course description and aims

This course gives lectures and practical exercises on machine learning algorithms and to cultivate the ability to implement the algorithm with actual data by using Python code. Specifically, this course introduces the basics of machine learning, unsupervised learning, and supervised learning. In addition, deep learning algorithms are also covered.

Student learning outcomes

Students will acquire the following skills by taking this course.
1) Be able to understand the basic concepts of machine learning algorithms and Python programing
2) Be able to select and implement machine learning methods for your own purposes


Machine learning, programming, Python, algorithm

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Brief lectures are given in the first half of each class. Then, students will tackle practical exercises on the given contents.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Python installation, basics of Python code Preparing a Python development environment for basic programming.
Class 2 Basics of machine learning, unsupervised learing and thier practical exercises Understanding the basics of machine learning and the procedures for unsupervised learning.
Class 3 Practical Exercise Practical exercise on unsupervised learning based on actual data.
Class 4 Basics of supervised learning and its exercise Understanding the basics of supervised learning.
Class 5 Practical Exercise Practical exercise on supervised learning based on actual data.
Class 6 Basics of deep learning and its exercise Understanding the basics of deep learning.
Class 7 Practical exercise Practical exercise on deep learning based on actual data.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.



Reference books, course materials, etc.

Course materials will be distributed at the class

Assessment criteria and methods

Assessed by the three exercises.

Related courses

  • SCE.Z401 : Cyber-Physical Innovation

Prerequisites (i.e., required knowledge, skills, courses, etc.)



Since this course has exercise, the number of students might be restricted in some case.

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