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.
Students will be able to acquire skills that is essential for creating applications of artificial intelligence, experiencing data processing and machine learning on computers.
classification, regression, gradient-based method, perceptron, activation function, backpropagation, automatic differentiation, convolutional neural network
|✔ Specialist skills||✔ Intercultural skills||Communication skills||Critical thinking skills||✔ Practical and/or problem-solving skills|
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|
|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|
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Cource materials are distributed via OCW-i.
Based on reports for given assignments.
When you apply this exercise, take "XCO.T489 Fundamentals of artificial intelligence", "XCO.T487 Fundamentals of data science" and "XCO.T488 Exercises in fundamentals of data science" of the same quarter of the same year in parallel. When there are many applicants, a lottery may be held. In the case of students of Tokyo Tech Academy for Convergence of Materials and Informatics, take “TCM.A404 Materials Informatics” instead of “XCO.T487 Fundamentals of data science” and “XCO.T488 Exercises in fundamentals of data science.
Students of the doctor course are required to register XCO.T680 "Exercises in fundamentals of progressive data science" instead of XCO.T490"Excersices in fundamentals of data science."
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.