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人工知能入門ａ(INTRODUCTION TO ARTIFICIAL
| 担当者名 | フィッツ， スティーブン |
|---|---|
| 単位 | 2 |
| 年度・学期 | 2024 春 |
| 曜日時限 | 金5 |
| キャンパス | 三田 |
| 授業実施形態 | 対面授業（主として対面授業） |
| 登録番号 | 83939 |
| 設置学部・研究科 | 経済学部 |
| 設置学科・専攻 | 経済学科 タイプA・B |
| 学年 | 3, 4 |
| 分野 | 専門教育科目選択特殊科目 |
| 評語タイプ | ログインすると表示されます（要慶應ID）。 |
| 科目概要 | 人工知能の分野を、特に深層ニューラルネットワークに焦点を当てて紹介し、現在のニューラルネットワーク構造、アルゴリズム、および最近の展開などについて議論する。 |
| K-Number | FEC-EC-35113-212-60 |
| 科目設置 | 学部・研究科 | FEC | 経済学部 |
|---|---|---|---|
| 学科・専攻 | EC | 経済学科 | |
| 科目主番号 | レベル | 3 | 3年次配当レベル |
| 大分類 | 5 | 専門教育 特殊科目 | |
| 小分類 | 11 | 講義 - 計量・統計 | |
| 科目種別 | 3 | 選択科目 | |
| 科目補足 | 授業区分 | 2 | 講義 |
| 授業実施形態 | 1 | 対面授業（主として対面授業） | |
| 授業言語 | 2 | 英語 | |
| 学問分野 | 60 | 情報科学、情報工学およびその関連分野 | |
授業科目の内容・目的・方法・到達目標
This is an introductory course on modern Artificial Intelligence designed for Keio University. We will focus predominantly on theory and fundamental concepts, with implementation of basic techniques in Python. Depending on the level of the students and time constraints, we might also cover more practical engineering topics using modern practices, as well as some of the most influential recent advancements based on a selection of research papers. Additionally, the course also covers some topics in more depth based on the interests of the instructor. One of those topics is Natural Language Processing (NLP) in the era of Deep Learning, as well as advanced methods in representation learning.
Recent years brought a revolution in the field of Artificial Intelligence on an unprecedented scale. Advances in hardware, availability of large data sets, as well as innovation in architectural and algorithmic design, enabled successful application of Machine Learning models based on multi-layered Artificial Neural Networks to a variety of problems of practical interest. The subfield of AI focusing on deep (multi-layered) neural architectures and the associated algorithms is collectively known as Deep Learning. The success of Deep Learning has been so great in recent years, that most of modern AI can be summarized as the study of deep neural architectures. Deep Learning models hold state-of-the-art (SOTA) results on virtually all AI tasks, and new discoveries are made almost daily. In many real world Machine Learning tasks, in particular those with perceptual input, such as vision and speech, the mapping from raw data to the output is often a complicated function with many factors of variation. In past approaches, to achieve decent performance on such tasks, significant effort had to be put to engineer hand-crafted features. Deep Learning algorithms aim to learn feature hierarchies with features at higher levels in the hierarchy formed by the composition of lower level features. This automatic learning of abstraction has been demonstrated to uncover underlying structure in the data (c.f. manifold hypothesis). The first success in this new era of connectionism, came in the fields of vision and speech processing. This was followed by developments in reinforcement learning, generative AI, and NLP (in particular large language models exhibiting surprising emergent properties).
This course aims to introduce students to the field of Artificial Intelligence, focusing on Deep Neural Information Processing Systems. Since this is a rapidly developing field, we will focus on most important trends and core ideas, as it is impossible to cover all recent developments in a single course. We will follow historical trends in AI with a focus on neural networks, and see how the current ideas emerged out of decades of research in the field. We will then discuss current neural architectures and algorithms, and introduce modern perspectives. After completing this course, students should have appreciation and understanding of neural AI systems, and anticipate future developments in research and applications of AI, and Deep Learning in particular. In addition to theory, there will be emphasis on programming skills in Python. Students will implement deep neural AI systems, and train them on standard data sets.
Recent years brought a revolution in the field of Artificial Intelligence on an unprecedented scale. Advances in hardware, availability of large data sets, as well as innovation in architectural and algorithmic design, enabled successful application of Machine Learning models based on multi-layered Artificial Neural Networks to a variety of problems of practical interest. The subfield of AI focusing on deep (multi-layered) neural architectures and the associated algorithms is collectively known as Deep Learning. The success of Deep Learning has been so great in recent years, that most of modern AI can be summarized as the study of deep neural architectures. Deep Learning models hold state-of-the-art (SOTA) results on virtually all AI tasks, and new discoveries are made almost daily. In many real world Machine Learning tasks, in particular those with perceptual input, such as vision and speech, the mapping from raw data to the output is often a complicated function with many factors of variation. In past approaches, to achieve decent performance on such tasks, significant effort had to be put to engineer hand-crafted features. Deep Learning algorithms aim to learn feature hierarchies with features at higher levels in the hierarchy formed by the composition of lower level features. This automatic learning of abstraction has been demonstrated to uncover underlying structure in the data (c.f. manifold hypothesis). The first success in this new era of connectionism, came in the fields of vision and speech processing. This was followed by developments in reinforcement learning, generative AI, and NLP (in particular large language models exhibiting surprising emergent properties).
This course aims to introduce students to the field of Artificial Intelligence, focusing on Deep Neural Information Processing Systems. Since this is a rapidly developing field, we will focus on most important trends and core ideas, as it is impossible to cover all recent developments in a single course. We will follow historical trends in AI with a focus on neural networks, and see how the current ideas emerged out of decades of research in the field. We will then discuss current neural architectures and algorithms, and introduce modern perspectives. After completing this course, students should have appreciation and understanding of neural AI systems, and anticipate future developments in research and applications of AI, and Deep Learning in particular. In addition to theory, there will be emphasis on programming skills in Python. Students will implement deep neural AI systems, and train them on standard data sets.
準備学修（予習・復習等）
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授業の計画
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成績評価方法
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テキスト（教科書）
Lecture notes should be sufficient, hence attending every lecture is highly recommended. Reading materials will be provided by the instructor. I expect students to attend all lectures, as part of the grade is based on class discussion, and I will often mention things in class which might be missing from written materials.
参考書
Lecture notes should be sufficient, hence attending every lecture is highly recommended. Reading materials will be provided by the instructor. I expect students to attend all lectures, as part of the grade is based on class discussion, and I will often mention things in class which might be missing from written materials.
担当教員から履修者へのコメント
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質問・相談
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