Essential Computer Science for Global LeadersUm17S1011n
|Essential Computer Science for Global LeadersUm17S1011n
Essential Computer Science for Global LeadersU
||BASHAR Md Khayrul
|Theory and practice; discussion and explanation using board and PPT slides
|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
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
|»ΜΌ=Tests (40%), Attendance (30%), Assignment (30%)
|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.
|Data Explorations (Four (4) classes)
• Introduction to data science, Exploratory data analysis and some EDA tools (Box plot, histogram, PCA etc.)
• Feature generation and feature types (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 (filters, wrappers and decision tree etc.)
• Interactive sessions or demonstration : data/ signal analysis (Matlab)
Data science and machine learning techniques and their applications (Six (6) classes)
• Machine learning basics ; Some machine learning algorithms (regression and classification : Bayesian, k- Nearest Neighbor (k-NN), Decision Tree, Support Vector Machine (SVM)).
• Interactive sessions or demonstration : Biometric recognition system / disease classification
• Introduction to artificial neural network ; Some neural network algorithms (Single & Multilayer perceptron (MLP), deep learning).
• Interactive sessions or demonstration : Biometric recognition system / disease classification/
• Assignement / Test
Internet-Of-Things (Last 5 classes)
• Introduction to arduino electronics and sketch; Programming basics on device control (C/C++) ;
• Develop a simple Arduino system for fruit quality detection
• Introduction to internet of things (IoT)
• Interactive sessions or demonstrations on human face detection and tracking using webcam-acquired video stream; car detection and traffic analysis.
• Final test or report
|Better to have general idea before each lecture
|Lecture will be delivered in both Japanese and English. Simple English will be used. Inquiries can be sent to Md. Khayrul Bashar at
Email : firstname.lastname@example.org;
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