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Codesort Course Titlesort Lecturersort Semestersort Week/Periodsort
4850-1022
GIF-MX6902L3
知能機械情報学特別講義II
Special Topics in Mechano-Informatics II
教員
S1S2 水曜4限
Wed 4th

The goal of this course is to provide advanced topics in the field of Machine Learning, Artificial Intelligence and Big Data. All lecturers are members of RIKEN Center for Advanced Intelligence Project (AIP).

単位 Credit:2
他学部 Other Faculty:可 YES
教室 Room: 工学部旧2号館 工233号講義室 Faculty of Engineering Bldg. old 2 #233
講義使用言語 Language:英語 English
3799-022
GEN-CO5900L1
先端技術特別講義II
Frontier of Technology Ⅱ
六川 修一
Shuichi RoKugawa
S1S2 水曜4限
Wed 4th

〔大学院・学部共通講義〕工学の知識と成果が応用される社会と、これらを具体的なシステムとして企画、研究・開発から導入展開までのサイクルを実行する現場での実際について、大学に居ながら肌で経験することは難しいと思います。本講義は、このようなサイクル全体における個々の活動及び総合的な活動を、学生が追体験して俯瞰的に理解し、現在から将来の活動に活かしていくための機会を提供します。このために、企業現場のプロジェクトリーダーを講師としてお招きし、成功例のみならず失敗例をも含めた実体験をもとに講義していただきます。社会における工学の意義、「今現在存在しないもの、新しいもの」をどのように発想したか、失敗をどのように克服したか等を横断的に学び、学生がタフで挑戦的な技術者・研究者として社会で活躍できるように支援します。 Advanced Engineering Education Series Seminar invites famous researchers / teachers in the field of advanced engineering education and advanced technology leaders, enabling students to experience advanced technologies directly from the leaders.

単位 Credit:2
他学部 Other Faculty:可 YES
教室 Room: 工学部新2号館 工212号講義室
講義使用言語 Language:日本語 Japanese
FEN-CO4952L1
FEN-CO4952L1
先端技術と社会特別講義II(偶数年開講)
Frontier of Technology UGⅡ
川中 孝章
S1S2 水曜4限
Wed 4th

〔大学院・学部共通講義〕工学の知識と成果が応用される社会と、これらを具体的なシステムとして企画、研究・開発から導入展開までのサイクルを実行する現場での実際について、大学に居ながら肌で経験することは難しいと思います。本講義は、このようなサイクル全体における個々の活動及び総合的な活動を、学生が追体験して俯瞰的に理解し、現在から将来の活動に活かしていくための機会を提供します。このために、企業現場のプロジェクトリーダーを講師としてお招きし、成功例のみならず失敗例をも含めた実体験をもとに講義していただきます。社会における工学の意義、「今現在存在しないもの、新しいもの」をどのように発想したか、失敗をどのように克服したか等を横断的に学び、学生がタフで挑戦的な技術者・研究者として社会で活躍できるように支援します。 Advanced Engineering Education Series Seminar invites famous researchers / teachers in the field of advanced engineering education and advanced technology leaders, enabling students to experience advanced technologies directly from the leaders.

単位 Credit:2
他学部 Other Faculty:可 YES
教室 Room: 工学部新2号館 工212号講義室
講義使用言語 Language:日本語 Japanese
4893-1001
GIF-CO5021L3
情報理工学英語特別講義Ⅰ(国民生活・社会基盤としてのITシステム特論)
Special Lecture in Information Science and Technology Ⅰ
教員
S1 月曜4限 水曜5限
Mon 4th Wed 5th

Many new services supporting our daily life composed of multiple systems via internet are prevailing. Society is increasingly dependent on software systems. Software failures and data incompatibility can cause or contribute to serious accidents that result in death, injury, serious environmental damage, or major financial loss. Such accidents have already occurred, and, without intervention, the increasingly pervasive use of software – especially in arenas such as transportation, health care, and the broader infrastructure –may make them more frequent and more serious. In the future, more pervasive development of software in the civic infrastructure could lead to more catastrophic failures unless improvements are made. On the other hand, so far worldwide research and studies have been suggesting us that software-related accidents are usually caused by flawed requirements; incomplete or wrong assumptions about operation of controlled system or required operation of computer, and unhandled controlled-system states and environmental conditions. Consequently, merely trying to get the software “correct” or to make it reliable (satisfy its requirements) will not make it safer under these conditions. In other words, preventing component or functional failure is NOT enough for maintaining or realizing system safety and security. This is a reason why currently systems engineering is coming to be focused on instead of conventional software engineering. In order to help the students understand the key technological issues for realizing dependable IT systems as public and socio-economic infrastructures, topics will be lectured by experts who are at the forefront from the following 3 aspects in this course. 1) Integrated systems as a social infrastructure - a large scale interconnected SoS (System of Systems) and Cyber Physical Systems (CPS) 2) Open data and interoperability infrastructure of Japanese e-Government 3) Systems Engineering for Cyber Security and System Safety

単位 Credit:2
他学部 Other Faculty:可 YES
教室 Room: 理7号館 102号室 Faculty of Science Bldg.7 Room#102
講義使用言語 Language:英語 English
5122159
GPP-MP6P20L3
Transformation of Warfare and Technology
Transformation of Warfare and Technology
青井 千由紀
AOI Chiyuki
A1A2 月曜4限
Mon 4th

There have developed quite separate debates concerning, on the one hand, the Transformation of Warfare, and Revolution in Military Affairs on the other. Conventional knowledge is that while the former approach encompasses a vast range of scholarly analysis on how war in the contemporary era might have transformed in all spheres of economy, politics and society, the latter has focused on the impact of technological advancement in a narrower sense on warfare, especially the way in which warfare has been conducted or ought to be conducted. However, to discuss changes in the character of war in these quite distinct spheres, each with specific preoccupations with no interactions, is not very conducive to a meaningful generalization about the transformation of warfare itself. The purpose of this seminar is to consider the impact of modern and contemporary technological changes, especially focusing on communication and media spheres, on the character and conduct of warfare today, hence bridging the two distinct spheres of interests.

単位 Credit:2
他学部 Other Faculty:可 YES
教室 Room: 国際学術総合研究棟 演習室B International Academic Research Bldg. Seminar Room B
講義使用言語 Language:英語 English
0510068
FSC-IS4068L3
情報と職業
Information and Profession
松本 隆明
Takaaki Matsumoto
S1 月曜4限 水曜5限
Mon 4th Wed 5th

Many new services supporting our daily life composed of multiple systems via internet are prevailing. Society is increasingly dependent on software systems. Software failures and data incompatibility can cause or contribute to serious accidents that result in death, injury, serious environmental damage, or major financial loss. Such accidents have already occurred, and, without intervention, the increasingly pervasive use of software – especially in arenas such as transportation, health care, and the broader infrastructure –may make them more frequent and more serious. In the future, more pervasive development of software in the civic infrastructure could lead to more catastrophic failures unless improvements are made. On the other hand, so far worldwide research and studies have been suggesting us that software-related accidents are usually caused by flawed requirements; incomplete or wrong assumptions about operation of controlled system or required operation of computer, and unhandled controlled-system states and environmental conditions. Consequently, merely trying to get the software “correct” or to make it reliable (satisfy its requirements) will not make it safer under these conditions. In other words, preventing component or functional failure is NOT enough for maintaining or realizing system safety and security. This is a reason why currently systems engineering is coming to be focused on instead of conventional software engineering. In order to help the students understand the key technological issues for realizing dependable IT systems as public and socio-economic infrastructures, topics will be lectured by experts who are at the forefront from the following 3 aspects in this course. 1) Integrated systems as a social infrastructure - a large scale interconnected SoS (System of Systems) and Cyber Physical Systems (CPS) 2) Open data and interoperability infrastructure of Japanese e-Government 3) Systems Engineering for Cyber Security and System Safety

単位 Credit:2
他学部 Other Faculty:可 YES
教室 Room: 理7号館 102号室 Faculty of Science Bldg.7 Room#102
講義使用言語 Language:英語 English
0704255
FEC-CE5801L3
横断型 University-wide
Deep Learning and Related Methods for Large Dataset Information Processing
Deep Learning and Related Methods for Large Dataset Information Processing(Graduate Level)
Fabinger Michal
Fabinger Michal
A1 月曜2限 金曜2限
Mon 2nd Fri 2nd
Deep Learning and Related Methods for Large Dataset Information Processing

Deep learning in artificial neural networks is a collection of statistical methods that benefit from large datasets and parallel computing. Recently it led to remarkable progress in many domains of research. This course provides an introduction to the subject, including the latest research. The structure of the course is chosen with the aim to be useful to students with very different academic backgrounds. Topics include: Optimization: backpropagation, stochastic gradient descent and its accelerated versions, second-order optimization methods. Supervised and semi-supervised machine learning: under-fitting and over-fitting, regularization, cross-validation, data augmentation. Neural network architecture: activation functions and their properties, layer patterns. Training neural networks: data preprocessing, weight initialization, gradient flow, batch normalization, regularization, practical aspects of GPU computing and distributed training. Hyper-parameter optimization, model ensembles, model compression. Transfer learning and fine-tuning. Spatial data modeling: convolutional networks, visualizing their internal data representations, susceptibility to adversarial examples. Sequence data modeling: recurrent networks, LSTMs, GRUs, and their convolutional alternatives, attention. Recursive data modeling: recursive neural networks. Natural language processing: word embedding and its visualization, neural machine translation, speech recognition and synthesis. Capsule networks. Unsupervised machine learning: variational autoencoders, adversarial networks, graphical models. Reinforcement learning: Q-learning, policy gradient methods and actor-critic methods, trust region policy optimization. Evolutionary strategies. Use of neural networks for designing and training other neural networks: neural architecture search, meta-learning. Hybrid computing combining advantages of neural networks and conventional computers. Use of deep learning for causal inference and counterfactual predictions. Privacy and ethical issues related to artificial intelligence. Selected applications: econometric estimation of causal effects, solutions to game-theoretic models, economic time-series modeling, sentiment analysis, patient health outcome prediction, low-cost disease diagnosis, overcoming sensory loss with deep-learning technologies. The course will include a first introduction to Python and to deep learning frameworks PyTorch, TensorFlow and Keras. The precise selection of topics for the course will be adjusted based on the students' interests.

単位 Credit:2
他学部 Other Faculty:可 YES
教室 Room: 国際学術総合研究棟 第8教室
講義使用言語 Language:英語 English
291324-04
GEC-EC6322L3
Deep Learning and Related Methods for Large Dataset Information Processing
Deep Learning and Related Methods for Large Dataset Information Processing
Fabinger Michal
Fabinger Michal
A1 月曜2限 金曜2限
Mon 2nd Fri 2nd
Deep Learning and Related Methods for Large Dataset Information Processing

Deep learning in artificial neural networks is a collection of statistical methods that benefit from large datasets and parallel computing. Recently it led to remarkable progress in many domains of research. This course provides an introduction to the subject, including the latest research. The structure of the course is chosen with the aim to be useful to students with very different academic backgrounds. Topics include: Optimization: backpropagation, stochastic gradient descent and its accelerated versions, second-order optimization methods. Supervised and semi-supervised machine learning: under-fitting and over-fitting, regularization, cross-validation, data augmentation. Neural network architecture: activation functions and their properties, layer patterns. Training neural networks: data preprocessing, weight initialization, gradient flow, batch normalization, regularization, practical aspects of GPU computing and distributed training. Hyper-parameter optimization, model ensembles, model compression. Transfer learning and fine-tuning. Spatial data modeling: convolutional networks, visualizing their internal data representations, susceptibility to adversarial examples. Sequence data modeling: recurrent networks, LSTMs, GRUs, and their convolutional alternatives, attention. Recursive data modeling: recursive neural networks. Natural language processing: word embedding and its visualization, neural machine translation, speech recognition and synthesis. Capsule networks. Unsupervised machine learning: variational autoencoders, adversarial networks, graphical models. Reinforcement learning: Q-learning, policy gradient methods and actor-critic methods, trust region policy optimization. Evolutionary strategies. Use of neural networks for designing and training other neural networks: neural architecture search, meta-learning. Hybrid computing combining advantages of neural networks and conventional computers. Use of deep learning for causal inference and counterfactual predictions. Privacy and ethical issues related to artificial intelligence. Selected applications: econometric estimation of causal effects, solutions to game-theoretic models, economic time-series modeling, sentiment analysis, patient health outcome prediction, low-cost disease diagnosis, overcoming sensory loss with deep-learning technologies. The course will include a first introduction to Python and to deep learning frameworks PyTorch, TensorFlow and Keras. The precise selection of topics for the course will be adjusted based on the students' interests.

単位 Credit:2
他学部 Other Faculty:可 YES
教室 Room: 国際学術総合研究棟 第8教室
講義使用言語 Language:英語 English
5175004
GPP-DP6P80L3
国際安全保障研究:政治学系(Transformation of Warfare and Technology)
International Security: Politics(Transformation of Warfare and Technology)
青井 千由紀
AOI Chiyuki
A1A2 月曜4限
Mon 4th

There have developed quite separate debates concerning, on the one hand, the Transformation of Warfare, and Revolution in Military Affairs on the other. Conventional knowledge is that while the former approach encompasses a vast range of scholarly analysis on how war in the contemporary era might have transformed in all spheres of economy, politics and society, the latter has focused on the impact of technological advancement in a narrower sense on warfare, especially the way in which warfare has been conducted or ought to be conducted. However, to discuss changes in the character of war in these quite distinct spheres, each with specific preoccupations with no interactions, is not very conducive to a meaningful generalization about the transformation of warfare itself. The purpose of this seminar is to consider the impact of modern and contemporary technological changes, especially focusing on communication and media spheres, on the character and conduct of warfare today, hence bridging the two distinct spheres of interests.

単位 Credit:2
他学部 Other Faculty:可 YES
教室 Room: 国際学術総合研究棟 演習室B International Academic Research Bldg. Seminar Room B
講義使用言語 Language:英語 English
5123041
GPP-MP6E20L3
Deep Learning
Deep Learning
Fabinger Michal
Fabinger Michal
A1 月曜2限 金曜2限
Mon 2nd Fri 2nd
Deep Learning and Related Methods for Large Dataset Information Processing

Deep learning in artificial neural networks is a collection of statistical methods that benefit from large datasets and parallel computing. Recently it led to remarkable progress in many domains of research. This course provides an introduction to the subject, including the latest research. The structure of the course is chosen with the aim to be useful to students with very different academic backgrounds. Topics include: Optimization: backpropagation, stochastic gradient descent and its accelerated versions, second-order optimization methods. Supervised and semi-supervised machine learning: under-fitting and over-fitting, regularization, cross-validation, data augmentation. Neural network architecture: activation functions and their properties, layer patterns. Training neural networks: data preprocessing, weight initialization, gradient flow, batch normalization, regularization, practical aspects of GPU computing and distributed training. Hyper-parameter optimization, model ensembles, model compression. Transfer learning and fine-tuning. Spatial data modeling: convolutional networks, visualizing their internal data representations, susceptibility to adversarial examples. Sequence data modeling: recurrent networks, LSTMs, GRUs, and their convolutional alternatives, attention. Recursive data modeling: recursive neural networks. Natural language processing: word embedding and its visualization, neural machine translation, speech recognition and synthesis. Capsule networks. Unsupervised machine learning: variational autoencoders, adversarial networks, graphical models. Reinforcement learning: Q-learning, policy gradient methods and actor-critic methods, trust region policy optimization. Evolutionary strategies. Use of neural networks for designing and training other neural networks: neural architecture search, meta-learning. Hybrid computing combining advantages of neural networks and conventional computers. Use of deep learning for causal inference and counterfactual predictions. Privacy and ethical issues related to artificial intelligence. Selected applications: econometric estimation of causal effects, solutions to game-theoretic models, economic time-series modeling, sentiment analysis, patient health outcome prediction, low-cost disease diagnosis, overcoming sensory loss with deep-learning technologies. The course will include a first introduction to Python and to deep learning frameworks PyTorch, TensorFlow and Keras. The precise selection of topics for the course will be adjusted based on the students' interests.

単位 Credit:2
教室 Room: 国際学術総合研究棟 第8教室
講義使用言語 Language:英語 English

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