Course title
4M6004001
Intelligent Information Processing

 nakamura shingo
Course content
Artificial intelligence technology has been rapidly developed, and expectations of it is growing. In particular, deep learning has been actively researched in last decade, and the results have been applied in various fields such as image processing, natural language processing, and robotics. In this course, the basics of artificial intelligence and machine learning, especially technologies of deep learning will be mentioned and students will learn how to create programs of deep learning using the programming language Python. Finally, some application examples of deep learning will introduced.
Purpose of class
The purpose of this class is to be able to create programs for deep learning after acquiring the basic knowledge of artificial intelligence and machine learning.
Goals and objectives
  1. To be able to explain the basics and problems of the artificial intelligence
  2. To be able to explain the technologies of the machine learning
  3. To be able to understand the basics of the deep learning
  4. To be able to create programs of the deep learning
  5. To be able to explain applications of the deep learning
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1． Introduction
Course abstraction
Artificial intelligence 		Read syllabus in advance 30minutes
(1) Report about artificial intelligence 100minutes
2． Machine Learning 1:
Optimization problem
Regression problem 		Review of optimization problem 60minutes
Review of regression problem 60minutes
3． Machine Learning 2:
Classification problem
Clustering problem
Dimension reduction 		Review of classification problem 60minutes
Review of clustering problem 60minutes
Review of dimension reduction 60minutes
4． Machine Learning 3:
Genetic algorithm
Reinforcement learning
Ensemble learning 		Review of genetic algorithm 60minutes
Review of ensemble learning 60minutes
(2) Report about machine learning 150minutes
5． Python 1
Basic grammar
Control statement
List 		Review of python basic grammar 200minutes
6． Python 2
Module
Class
External library 		(3) Python programming assignment 200minutes
7． Neural Networks 1:
Multi-layered perceptron
Back-propagation method 		Review of multi-layered perceptron 100minutes
Review of back-propagation method 100minutes
8． Neural Networks 2:
Programming multi-layered perceptron 		(4) Programming assignment about multi-layered perceptron 200minutes
9． Deep Learning Basics:
Learning method
Optimization algorithm
Convolutional neural networks 		Review learning method 60minutes
Review optimization algorithm 60minutes
Review convolutional neural network 60minutes
10． Deep Learning Program:
Framework for deep learning
Creating a program 		(5) Programming assignment of deep learning 200minutes
11． Deep Learning Application 1:
Image processing
Dataset 		Review image processing 200minutes
12． Deep Learning Application 2:
Creating a program of image processing 		(6) Programming assignment of image processing 200minutes
13． Deep Learning Application 3:
Natural language processing
Recurrent neural network 		Review natural language processing 200minutes
14． Deep Learning Application 4:
Generative model
Generative adversarial network 		Review generative model 200minutes
Total. - - 2680minutes
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Report Programming Total.
1. 10% 0% 10%
2. 10% 0% 10%
3. 10% 10% 20%
4. 0% 40% 40%
5. 10% 10% 20%
Total. 40% 60% -
Evaluation method and criteria
Report assignments 60%
Programming assignments 60%
Textbooks and reference materials
To purchase textbooks is unnecessary.
Reference materials are distributed for each lecture.
Prerequisites
It is desirable to have experiences of high-level language, such as C and Java.
In particular, it is better to understand control statement ("if", "for" and "while") and function / method.
Office hours and How to contact professors for questions
  • Before and after lectures
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
  • Course that cultivates a basic problem-solving skills
Active-learning course
About half of the classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
Applicable Taking advantage of experience of software development, I teach programming for deep learning.
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Sun Mar 21 16:50:20 JST 2021