2023 Fundamentals of Machine Learning

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Academic unit or major
Undergraduate major in Systems and Control Engineering
Itoyama Katsutoshi 
Class Format
Lecture    (Face-to-face)
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

In this lecture, we will learn some basic methods in machine learning, their mathematical derivations, and their implementation on computers.

Student learning outcomes

Understand typical machine learning tasks such as regression, classification, and clustering, and learn methods to perform these tasks and be able to implement and run them on a computer.


Machine Learning, Artificial Intelligence, Deep Learning, Pattern Recognition

Competencies that will be developed

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

Class flow

Classes mainly consist of lectures and exercises.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction to machine learning, mathematics and programmng fundamentals for machine learning Building a machine leaning programming environment
Class 2 Supervised learning (regression and classification) Programming for supervised learning
Class 3 Unsupervised learning (clustering and dimension reduction) Programming for unsupervised learning
Class 4 Reinforcement learning Programming for reinforcement learning
Class 5 How to handle various types of data Programming to handle various types of data
Class 6 Neural network (basic) Programming for neural networks
Class 7 Neural network (advanced) Programming for neural networks

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.


No textbook used

Reference books, course materials, etc.

Reference Book: Pattern Recognition and Machine Learning, C. M. Bishop, Springer

Assessment criteria and methods

Students will be assessed on their understanding of the basic theory of machine learning and its application. Exercise problems 70%, final examination 30%.

Related courses

  • SCE.I204 : Information Processing and Programming (Systems and Control)
  • SCE.I205 : Fundamentals of Data Science
  • LAS.M101 : Calculus I / Recitation
  • LAS.M102 : Linear Algebra I / Recitation
  • SCE.M307 : Image Sensing

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

It is desirable to have a basic knowledge of linear algebra and calculus.

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