2022 Fundamentals of Progressive Artificial Intelligence

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Academic unit or major
School of Computing
Okazaki Naoaki  Shimosaka Masamichi  Sekijima Masakazu  Nitta Katsumi  Nagahashi Hiroshi  Kobayashi Takao  Miyake Yoshihiro 
Class Format
Lecture    (Livestream)
Media-enhanced courses
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Course description and aims

Artificial Intelligence is a research area that aims at artificially creating intelligence like humans. In recent years, artificial
intelligence was successfully applied to various domains with the advances on machine learning and deep learning utilizing big data and computation power. This lecture expects students to acquire knowledge that is essential for creating applications of artificial intelligence, explaining basic concepts and theories of artificial intelligence.

Student learning outcomes

Students will be able to acquire knowledge that is essential for creating applications of artificial intelligence.


classification, regression, gradient-based method, perceptron, activation function, backpropagation, automatic differentiation,
convolutional neural network

Competencies that will be developed

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

Class flow

All classes are given as Zoom lectures.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Essential Mathematics for Machine Learning Artificial Intelligence applied to the real world
Class 2 Essential Mathematics for Machine Learning Linear Algebra, Probability Theory and Statistics, Calculus
Class 3 Linear Regression Loss function, empirical risk minimization, overfitting,regularization,bias and variance,linear model (regression),ridge regression
Class 4 Linear Classification Linear model (classification),logistic regression, gradient methods
Class 5 Single-layer Neural Network single-layer perceptron, activation functions, computational graph, automatic differentiation
Class 6 Multi-layer Neural Network multi-layer perceptron, hidden units, backpropagation, softmax function
Class 7 Convolutional Neural Network convolutional neural network, dropout

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 are distributed via T2SCHOLA.

Assessment criteria and methods

Based on multiple times of the reports and the final report

Related courses

  • XCO.T490 : Exercises in fundamentals of artificial intelligence
  • XCO.T483 : Advanced Artificial Intelligence and Data Science A
  • XCO.T485 : Advanced Artificial Intelligence and Data Science C
  • XCO.T486 : Advanced Artificial Intelligence and Data Science D

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

Preferred to have basic knowledge about linear algebra, analysis, and mathematical statistics.
This course is given for the doctor course students. To register both this course and XCOT.T489 at once is not allowed.

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