Print

2023　Basics and Applications of Data Science and Artificial Intelligence II

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
School of Computing
Instructor(s)
Kobayashi Takao  Nitta Katsumi  Miyazaki Kei  Tomii Norio  Okumura Keiji  Sakuma Jun  Ono Isao  Miyake Yoshihiro  Hasegawa Kei 
Class Format
Lecture    (Livestream)
Media-enhanced courses
Courses specified
Day/Period(Room No.)
Wed7-8(W2-401(W241))  
Group
-
Course number
XCO.T282
Credits
1
Academic year
2023
Offered quarter
2Q
Syllabus updated
2023/3/20
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course gives basic theories, methods, and algorithms of data science, data engineering, and AI to students those who have completed the course "Basics and Applications of Data Science and Artificial Intelligence I" in the first quarter. The curriculum is designed so that it provides an intermediate-level course study of data science and AI between literacy- and expert-level ones. The course would enable students to understand theories and methods deeply and achieve practical skills in problem solving through a variety of examples and exercises.

Student learning outcomes

Students will be able to:
1) Understand significance of studying data science, as well as data analysis methods, and choose appropriate data analysis and visualization methods.
2) Understand roles of data engineering, representation methods of various data on a computer, and data acquisition/processing/accumulation techniques.
3) Understand history of AI, its technical background, AI ethics, machine learning and learning algorithms, neural networks and deep learning algorithms, and apply AI technology to problem solving.

Keywords

Population, inferential statistics, statistical interference, statistical test, unsupervised leaning, supervised leaning, reinforcement learning, DNN, CNN, RNN, recognition, prediction

Competencies that will be developed

✔ Specialist skills ✔ Intercultural skills Communication skills ✔ Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

To check students’ understanding, students are assigned exercises at every class.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Fundamentals of mathematical statistics, part 1 Understand basics of mathematical statistics and also learn statistical interference methods through specific examples.
Class 2 Fundamentals of mathematical statistics, part 2 Learn the basic theory of hypothesis testing and understand hypothesis testing methods through specific examples.
Class 3 Machine learning, part 1 Understand basics of machine learning algorithms by applying them to practical problems such as classification and clustering.
Class 4 Machine learning, part 2 Understand basics of machine learning algorithms in which topics include supervised learning, cross validation, overfitting, and reinforcement learning.
Class 5 Neural networks and deep learning, part 1 Understand principles of artificial neural networks and their training algorithms in which topics includes perceptron, multilayer perceptron, and back propagation algorithm.
Class 6 Neural networks and deep learning, part 2 Understand structures and mechanisms of useful neural networks such as deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN).
Class 7 AI applications Understand roles of AI technology in our daily life by looking at a wide variety of examples to which AI technology has been successfully applied.

Out-of-Class Study Time (Preparation and Review)

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.

Textbook(s)

None. Lecture materials will be given in the class.

Reference books, course materials, etc.

Lecture materials will be found on T2SCHOLA in advance and shared in Zoom lecture.

Assessment criteria and methods

Grading is based on exercises and term-end report.

Related courses

  • LAS.I111 ： Information Literacy I
  • LAS.I112 ： Information Literacy II
  • LAS.I121 ： Computer Science I
  • LAS.I122 ： Computer Science II
  • LAS.I131 ： Basics of Data Science and Artificial Intelligence
  • LAS.M101 ： Calculus I / Recitation
  • LAS.M102 ： Linear Algebra I / Recitation
  • LAS.M105 ： Calculus II
  • LAS.M106 ： Linear Algebra II
  • XCO.T281 ： Basics and Applications of Data Science and Artificial Intelligence I

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students are assumed to have the knowledge given in Calculus I and II, Linear Algebra I and II, Basics of Data Science and Artificial Intelligence, and Basics and Applications of Data Science and Artificial Intelligence I.

Contact information (e-mail and phone)    Notice : Please replace from "[at]" to "@"(half-width character).

KOBAYASHI, Takao (lecture_ba_2023[at]dsai.titech.ac.jp)

Office hours

Students can ask questions on T2SCHOLA forum or by e-mail.

Page Top