Artificial intelligence technology has been rapidly developed, and expectations of it are growing. In particular, deep learning
has been actively researched in the 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. Students will learn how to create programs of deep learning using the programming
language Python. Finally, some application examples of deep learning will be introduced.
It is desirable to have experience with high-level languages, such as C and Java. In particular, it is better to understand the control statements (if, for, while), array, and function/method.
All lectures will be conducted online.
It is desirable to have experience with high-level languages, such as C and Java. In particular, it is better to understand the control statements (if, for, while), array, and function/method.
All lectures will be conducted online.
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.
- To be able to explain the basics and problems of the artificial intelligence
- To be able to explain the technologies of the machine learning
- To be able to understand the basics of the deep learning
- To be able to create programs of the deep learning
- To be able to explain applications of the deep learning
| 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 (MLP) Back-propagation method (BP) |
Review of MLP | 100minutes |
| Review of BP 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: Convolutional Neural Network (CNN) AutoEncoder (AE) |
Review of CNN and AE | 200minutes |
| 12． | Deep Learning Application 2: Variational AutoEncoder (VAE) |
(6) Programming assignment about VAE | 200minutes |
| 13． | Deep Learning Application 3: Generative Adversarial Network (GAN) |
Review natural language processing | 200minutes |
| 14． | Deep Learning Application 4: Natural language processing (NLP) Recurrent neural network (RNN) |
Review of NLP | 200minutes |
| Total. | - | - | 2680minutes |
| Report | Programming | Total. | |
|---|---|---|---|
| 1. | 10% | 0% | 10% |
| 2. | 20% | 0% | 20% |
| 3. | 10% | 10% | 20% |
| 4. | 0% | 30% | 30% |
| 5. | 10% | 10% | 20% |
| Total. | 50% | 50% | - |
Report assignments 50%
Programming assignments 50%
A score higher than 60pt passes
60pt is the level at which basic techniques of deep learning can be explained.
Programming assignments 50%
A score higher than 60pt passes
60pt is the level at which basic techniques of deep learning can be explained.
| ways of feedback | specific contents about "Other" |
|---|---|
| The Others | 授業外(Scombz・メール等)でフィードバックをを行います |
To purchase textbooks is unnecessary.
Reference materials are distributed for each lecture.
Reference materials are distributed for each lecture.
It is desirable to have experience in a high-level language, such as C or Java.
In particular, it is better to understand control statements ("if", "for" and "while") and function/method.
Prepare your PC for programming.
In particular, it is better to understand control statements ("if", "for" and "while") and function/method.
Prepare your PC for programming.
- Course that cultivates an ability for utilizing knowledge
- Course that cultivates a basic problem-solving skills
| Work experience | Work experience and relevance to the course content if applicable |
|---|---|
| Applicable | I will teach the programming of deep learning through my experience in application software development |
- 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
- 12.RESPONSIBLE CONSUMPTION & PRODUCTION
Last modified : Tue Sep 12 04:06:31 JST 2023