2022 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
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Academic year
Offered quarter
Syllabus 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

Learn about neural networks and clustering, which are the basic methods of machine learning, and acquire the mathematical basics for deriving them so that they can be implemented and operated 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 Basics of mathematics required for machine learning
Class 2 Linear models (Regression) Linear regression model and its learning method
Class 3 Linear Models (Classification) Logistic regression, support vector machine
Class 4 Neural Network (Basic) Perceptron, activation function, back propagation method
Class 5 Neural Network (Advanced) Convolutional neural network (CNN), recurrent neural network (RNN)
Class 6 Clustering k-means, Gaussian mixture model (GMM)
Class 7 Matrix Decomposition Principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF)

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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