Information Processing 2

Course Information

College	Anan College	Year	2024
Course Title	Information Processing 2
Course Code	1295801	Course Category	Specialized / Elective
Class Format	Lecture	Credits	Academic Credit: 2
Department	Course of Mechanical Engineering	Student Grade	5th
Term	First Semester	Classes per Week	前期:2
Textbook and/or Teaching Materials	ニューラルネットワーク自作入門 (マイナビ)
Instructor	Matsuura Fuminori

Course Objectives

1. Can explain the training of prediction machines and classifiers as well as backpropagation of errors.
2. Can implement a neural network with input, hidden, and output layers, capable of identifying handwritten digits.

Rubric

	Ideal Level	Standard Level	Unacceptable Level
Principle	Can derive the matrix form for output layer computation and weight updates using the backpropagation method.	Understands and can explain the matrix form for output layer computation and weight updates using the backpropagation method.	Can provide a general explanation of the method for updating weights using the output layer computation method and the backpropagation method.
Implementation in Python	Can identify one's own handwritten digits.	Can identify handwritten digits using the MNIST dataset.	Can implement a simple neural network.

Assigned Department Objectives

学習・教育到達度目標 B-4 （情報技術に関する知識を利活用できる。）

学習・教育到達度目標 D-1 （設計・システム系，情報・論理系，材料・バイオ系，力学系を含む工学の基礎となる学問分野について，自主的かつ継続的に学習できる。）

Teaching Method

Outline:

Can explain classifiers and their learning methods based on neural networks, which are fundamental to deep learning (deep neural networks), and can implement them in Python.

Style:

Students are assumed to have already acquired knowledge of matrix inner products and basics of Python (functions, matrix operations using Numpy) through other classes or self-study.
【Lecture hours: 30 hours + Self-study hours: 60 hours】

Notice:

This course is designed for individuals who have acquired linear algebra and programming skills, aiming to learn the principles used in machine learning (artificial intelligence). There is a significant portion where matrix operations are carried out by hand, demanding not only proficiency in matrix operations but also "grit."

Characteristics of Class / Division in Learning

Applicable to Remote Class

Course Plan

			Theme	Goals
1st Semester
	1st Quarter
		1st	Simple Prediction Machine	Can explain methods for learning predictive machines.
		2nd	Learning a Simple Classifier	Can explain how to use classifiers to categorize data.
		3rd	Neuron	Can describe the structure of a neuron and networks formed by neurons.
		4th	Learning Weights from Two or More Nodes	Can explain how to adjust the internal parameters of a neuron.
		5th	Backpropagation from Many Nodes	Can explain how to adjust the internal parameters of a neural network with many nodes.
		6th	Backpropagation to Many Layers	Can explain how to propagate errors from the output layer to the hidden layers.
		7th	Midterm Exam
		8th	Weight Update	Can explain the equations used for updating the weights of an entire neural network.
	2nd Quarter
		9th	Introduction to Python	Can create Python programs using classes, among other things, to implement neural networks.
		10th	Definition of Neural Network Class 1	Can implement the structure of a neural network.
		11th	Definition of Neural Network Class 2	Can implement a neural network that is capable of backpropagation.
		12th	Learning the Network	Can train a neural network and visualize the process.
		13th	Handwritten Digit Dataset 1	Can explain what the MNIST dataset of handwritten digits is.
		14th	Handwritten Digit Dataset 2	Can implement a neural network that recognizes handwritten digits.
		15th
		16th

Evaluation Method and Weight (%)

	Examination	Presentation	Mutual Evaluations between students	Behavior	Portfolio	Other	Total
Subtotal	100	0	0	0	0	0	100
Basic Proficiency	0	0	0	0	0	0	0
Specialized Proficiency	100	0	0	0	0	0	100
Cross Area Proficiency	0	0	0	0	0	0	0