Essential Computer Science for Global Leaders Ⅱ 2019年10月7日開講 

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10月7日より、Essential Computer Science for Global LeadersⅡを開講します。この科目はグローバル理工学副専攻の履修科目になっていますが、博士前期・後期課程に所属し、関心のある学生であれば、どなたでも履修することができます。なお、講義は英語で行われます。

本講座の主旨

Bashar特任准教授Steady increase of the deployment of computer systems in many real world applications made computer science and engineering an inevitable discipline in the current epoch of human history. Along with electronics, it drives the information revolution following industrial and agricultural revolutions. Future progress and the ultimate shape of this planet will largely depend on how the next generation global leaders are going to be equipped with essential knowledge on computer science and engineering. In this course, light will be shed on some advanced topics involving information security, artificial intelligence, the design and control of electronic devices for some real world applications.

学生へのメッセージ

Lecture will be delivered in both Japanese and English. Simple English will be used. Inquiries can be sent to Md. Khayrul Bashar at
Email : basha.md.khayrul@ocha.ac.jp
Tel : 03-5978-2557 ;
Office : Science Building – 3 (Room : in front of Elevator Door at 3rd Floor)
N.B. Contents or the extent of the topics may be refined subject to necessity

講義概要

科目名
Essential Computer Science for Global LeadersⅡ [19S1011]
単位数
2.0単位
担当教員
BASHAR, Md Khayrul (お茶の水女子大学リーディング大学院推進センター特任准教授)
対象
博士前期・後期課程
場所
人間文化研究科棟408室
日時
月曜 3~4限(10:40-12:10)
1月16日(木)を除く
2019年
 10月7日、21日、28日
 11月4日、11日、18日、25日
 12月2日、9日、16日、23日
2020年
 1月6日、16日、20日、27日
授業計画

(a) Data Explorations (Four (4) classes)

  • Introduction to data science, data types, typical data analysis methods.
  • Feature extraction from image data (Local Binary Pattern (LBP), Histogram of Oriented Gradient (HOG), Scale Invariant Feature Transform (SIFT) and other transforms (Fourier transform, wavelet transform etc.)
  • Feature selection and related algorithms
  • Practice sessions on data analysis (C++/Matlab/Python/R)

(b) Machine learning and applications (Six (6) classes)

  • Introduction, Some machine learning algorithms: minimum distance to mean, k- Nearest Neighbor, Maximum Likelihood and Naive Bayes, linear discriminant analysis (LDA), Decision Tree, Support Vector Machine (SVM)).
  • Basics of artificial neural network (ANN) ; Some neural network algorithms (single and multilayer perceptron (MLP), deep learning).
  • Practice sessions on machine learning algorithms.
  • Assignment / Test

(c) Internet-Of-Things (IoT) (Five (5) classes)

  • Introduction, Brief history of IoT, How it works ?;
  • Structure of IoT; Current status and future prospect;
  • Examples of IoT (fruit quality detection; human face tracking using webcam; car detection and traffic analysis)
  • Final test/report

 
NB: Contents may be revised or modified subject to necessity.

時間外学習
Having general idea before each lecture may be useful.
教科書・参考文献
  1. Computer Vision: Algorithm and Applications – Richard Szeliski
  2. S. Haykin, Neural Networks: A comprehensive Foundation, MacMillan College Publishing Co. New York, 1994
  3. C++ How to Program by Paul Deitel and Harvey Deitel
  4. Arduino Sketches: Tools and Techniques for programming Wizardry – James A. Langbridge
  5. Make: JavaScript Robotics — Backstop Media and Rick Waldron
  6. Signal Processing for Neuroscientists – Wim van Drongelen
  7. Rajkumar Buyya: Internet of Things — Principles and Paradigm, Morgan Kaufmann, Elsevier, USA, 2016.
  8. Lecture materials will also be supplied whenever needed

履修登録

履修登録期間: 10月 1日(火)~ 10月14日(月)
上記登録期間内に登録ができなかった場合は、学生センター棟1階学務課にご相談ください。
※学部生の聴講については、リーディング大学院推進センターにご連絡ください。

お問合せ

お茶の水女子大学 リーディング大学院推進センター
Tel: 03-5978-5775
E-mail: