- NUS School of Computing Summer Workshop 2024
- NUS_SoC is a repo containing materials and projects of SoC, NUS
Attend real-time online introductory lectures of all the courses in our cluster via Zoom to have a sense of what we will be getting in each course. Lectures will be conducted on Sundays (12 May, 19 May & 26 May). Expect some assignments and quizzes at this stage.
My Claster: Cluster: Cloud, Security, IoT & AI
- Cloud Computing with Big Data
- DOTA Defense of the Ancients
- Robotics
- Deep Learning
- Artificial Intelligence of Things
- Visual Computing
You can learn a detailed information such as slides, codes and references in the corresponding folder.
In Phase 2, we will be coming to NUS School of Computing to join us in an exciting on-campus learning experience. By now, we are assigned to a single course, and will be focusing on this topic by attending advanced seminars and working on a group project under the supervision of our course instructor.
I will showcase and update the entire codes in this part after finishing SoC.
Mainly focus on "Computational Photography" & "Pattern Recognition" & "3D Reconstruction"
Background
- Computer Programming
- Algorithms & Data Structures
- Linear Algebra
- Probability & Statistics
- Machine Learning
- Image Processing
- Computer Vision
- Computer Graphics
- Python or C/C++
- OpenCV
- Numpy/Scipyor Dlib
- PyTorch/Keras/Tensorflow
Better
- Computer graphics
- Optimization
- Multivariate Calculus
Brief Intro
Artificial Intelligence of Things (AIoT) lies at the intersection of Artificial Intelligence (AI)technologies and Internet of Things (IoT) infrastructure. AIoT aims to achieve smart IoT operations that optimise human-machine interaction, and data management and analytics.
More specifically, IoT is set to disrupt the way we live and work. Smart homes that are filled with connected devices are loaded with endless possibilities to make our lives easier, more convenient, and more comfortable. Industry 4.0, which is powered by Industrial IoT (IIoT), promises to turn smart manufacturing and smart factory into a reality.
IoT devices are expected to generate a huge volume of data. AI techniques such as machine learning and deep learning can help individuals and organisations alike to realise unprecedented business values from these data.
In this course, you will learn how to work with single-board microcontrollers and computers in conjunction with various connected devices such as sensors, actuators, smartphones, smartwatches, Bluetooth Low Energy beacons, and other interesting hardware to build various smart home and industry scenarios. You will also learn how to integrate a real-time data pipeline for visualising and analysing the data that are collected by these devices to create a smart AIoT system.
Background
- Machine Learning
- Networks
- Python
- micro:bit
Necessity
Often the same security problems that occur in society re-occur today in computer systems: there are many examples of computer-based security activities that we can find bylooking at society, or by studying history books. For example, confidentiality problems result in concerns about locks, and en-coding. Integrity problems result in concerns about signatures,and handshakes. In each of these, we can see simple examplesfrom society, and the computer-based versions follow the samelines (only a million times faster)
Prospect
In any of these, you can see that there are a wide range ofactivities (and hence jobs): Information Security Engineer, IT Security Architect, IT Security Specialist, IT Security Analyst, Business Security Manager, Security Research (Technical).
Roadmap
General Concepts of Cloud Computing
- Cloud and cloud computing
- Service models
Virtualization Technologies
- Virtual Machines
- Containers
Container Orchestration
- Introduction to Kubernetes
Prerequisites
- Programming Languages, e.g., Python, Java, Go
- Operating Systems, i.e., Linux
- Computer Networks, e.g., L2 and L3 networking
Cloud Resources
- Trial on real production cloud: Amazon EC2
- Each student will receive US$100 credits for the use of AWS services; available in July
- Credit card tie-in not required
Course Objectives
- An appreciation of embedded system platforms
- Understand the process of incremental, modular system design
- Ability to implement a solution that impact the physical world
Roadmap
This Part: The “brain” of the robot
- Machine learning:
- Statistical Methods.
- Neural Networks.
- Deep Learning Networks.
- Communications.
- Reverse tunnels: Control your robot from any part of the world
- Transport layer security