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- Academic unit or major
- School of Computing
- Instructor(s)
- Okazaki Naoaki Shimosaka Masamichi Inoue Nakamasa Ora Hiroki Chang Shuang Sekijima Masakazu Yanagisawa Keisuke Yasuo Nobuaki
- Class Format
- Exercise
- Media-enhanced courses
- Day/Period(Room No.)
- Thr7-8(学術国際情報センター3F第1実習室,, すずかけ台情報ネットワーク演習室)
- Group
- -
- Course number
- XCO.T490
- 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 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 excercises 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 | multi-layer perceptron, hidden units, backpropagation, softmax function |
| Class 7 | Convolutional Neural Network | convolutional neural network, dropout |
| Class 8 | Discussion |
Textbook(s)
None
Reference books, course materials, etc.
Cource materials are distributed via OCW-i.
Assessment criteria and methods
Based on reports for given assignments.
Related courses
- XCO.T489 ： 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.)
Take a prerequirement exam about "linear algebra", "analysis", and "basic grammar and functions of Python3" in the first class on Monday, December 2, 2019. Make sure to come to W531 or G115 no later than 15:05. You will not be allowed to take this course if you skip this exam, and may not be allowed depending on its score. It is also mandatory to take "XCO.T489 Fundamentals of artificial intelligence" and "XCO.T488 Exercises in fundamentals of data science" in parallel.
Other
A prerequirement test is conducted in irregular room W531 or G115 in the first class of "XCO.T488 Exercises in fundamentals of data science" on Monday, December 2nd. Exercises are carried out using Google Colaboratory. Students are required to get Google accounts and to get ready for using "file upload/download" in Google Drive.
