2019 Fundamentals of artificial intelligence

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Academic unit or major
School of Computing
Instructor(s)
Okazaki Naoaki  Shimosaka Masamichi  Inoue Nakamasa  Sekijima Masakazu  Yanagisawa Keisuke  Yasuo Nobuaki 
Course component(s)
Lecture
Day/Period(Room No.)
Thr5-6(W531,G115)  
Group
-
Course number
XCO.T489
Credits
1
Academic year
2019
Offered quarter
4Q
Syllabus updated
2019/11/15
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 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.

Keywords

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

Competencies that will be developed

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

Class flow

All classes are given in both Ookayama and Suzukakedai campuses with the use of video conference systems.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Class guidance 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
Class 8 Final examination

Textbook(s)

None

Reference books, course materials, etc.

Cource materials are distributed via OCW-i.

Assessment criteria and methods

Based on the final exam.

Related courses

  • XCO.T490 : Exercises in fundamentals of artificial intelligence
  • XCO.T483 : Advanced Artificial Intelligence and Data Science A
  • XCO.T484 : FinTech and Data Science
  • XCO.T485 : Advanced Artificial Intelligence and Data Science C
  • XCO.T486 : Advanced Artificial Intelligence and Data Science D
  • XCO.T487 : Fundamentals of data science
  • XCO.T488 : Exercises in fundamentals of data science

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

Preferred to have basic knowledge about linear algebra, analysis, and mathematical statistics.

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