2021 (Exercises in fundamentals of progressive artificial intelligence)

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Academic unit or major
School of Computing
Instructor(s)
Yanagisawa Keisuke  Okazaki Naoaki  Shimosaka Masamichi  Sekijima Masakazu  Nitta Katsumi  Nagahashi Hiroshi  Kobayashi Takao  Miyake Yoshihiro 
Course component(s)
Exercise     
Day/Period(Room No.)
Mon7-8()  
Group
-
Course number
XCO.T680
Credits
1
Academic year
2021
Offered quarter
3Q
Syllabus updated
2021/9/17
Lecture notes updated
-
Language used
Japanese
Access Index

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 skills that is essential for creating applications of artificial intelligence, implementing basic concepts and theories as a computer program.

Student learning outcomes

Students will be able to acquire skills that is essential for creating applications of artificial intelligence, experiencing data processing and machine learning on computers.

Keywords

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

In class, students are required to solve exercises that are linked with the contents of taught course "XCO.T487 Fundamentals of
data science".

Course schedule/Required learning

  Course schedule Required learning
Class 1 Class guidance and setup of computing environment 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 single-layer perceptron, activation functions, computational graph, automatic differentiation
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.

Textbook(s)

None

Reference books, course materials, etc.

Course materials are distributed via OCW-i.

Assessment criteria and methods

Based on reports for given assignments.

Related courses

  • XCO.T679 : Fundamentals of progressive artificial intelligence
  • XCO.T483 : Advanced Artificial Intelligence and Data Science A
  • XCO.T485 : Advanced Artifi cial Intelligence and Data Science C
  • XCO.T486 : Advanced Artificial Intelligence and Data Science D
  • XCO.T677 : Fundamentals of progressive data science
  • XCO.T488 : Exercises in fundamentals of data science

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

This exercise is for the doctor course students. When you apply this exercise, take "XCO.T679 Fundamentals of advanced artificial intelligence", "XCO.T677 Fundamentals of advanced data science" and "XCO.T678 Exercises in fundamentals of advanced data science" of the same quarter of the same year in parallel. When there are many applicants, a lottery may be held.

Other

Exercises are carried out using Google Colaboratory. Students are required to get Google accounts and to get ready for using
"fileupload/download" in Google Drive.

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