Topics Covered in the Course
As you explore the course, you will meet and learn with other people who are also interested in artificial intelligence and/or machine learning discussing and exploring topics such as:
- What are we talking about when we talk about AI?
- The faces of AI
- Games IRL
- Drone Delivery
- Smart Home
- AI today and tomorrow
- Your journey in this course
- What are we asking AI to do?
- Who
- What and Where
- How
- Finding a solution to a (discrete) problem
- Searching the environment
- Drones in (discrete) space!
- Path searches
- Searching the space
- Searching for good positions
- Smarter searching
- Optimisation, or, Achieving an objective (even in a continuous space!)
- Achieving an objective
- Constraint satisfaction problems
- Solving constraint satisfaction problems
- Local searches and optimisation
- Continuous space optimisation
- Gradient methods
- Other continuous space optimisation methods
- Genetic algorithms
- Crossing the Rubicon
- Machine learning
- What is machine learning?
- Why do we use machine learning?
- How does machine learning technology work?
- Artificial Neural Networks: Input layer
- Artificial Neural Networks: Hidden layers
- Artificial Neural Networks: Output layer
- Supervised learning: the basics
- Fun with supervised learning
- Classification and diagnostics
- Spam filtering
- Reading handwriting
- Basic speech recognition
- Medical diagnosis
- Reinforcement learning: the basics
- Fun with reinforcement learning
- Drone Delivery: The game!
- Drone Delivery: States, actions and rewards
- Drone Delivery: Q-learning
- Drone Delivery: Exploration and exploitation
- Drone Delivery: Deep Q-learning
- Supervised learning: further exploration
- Valuation of houses (and all sorts of things)
- Using one variable to estimate another
- Multiple input variables
- Learning regression and overfitting
- Reinforcement learning: further exploration
- Moving about in the real world
- Robotic movement: Today's challenges
- Unsupervised learning
- Fun with unsupervised learning
- Customer segmentation
- k-means clustering
- Better processing of images (and sound)
- Auto-encoders and dimensionality reduction
- Denoising (noise reduction)
- Discrete Graph Search
- Discrete Graph Search algorithms
- Depth-First Search
- Breadth-First Search
- Uniform Cost Search
- Best-First Search
- A* Search
- Motion Planning
- Rapidly Exploring Random Trees (RRTs)
AboutSenior Lecturer Alan Blair
This UNSW short course has been designed in collaboration with Alan Blair, a senior lecturer at the Faculty of Engineering at UNSW Sydney in the School of Computer Science and Engineering.
At UNSW, Alan is the lecturer in charge of two undergraduate/postgraduate subjects in the field of artificial intelligence, including: COMP3411/9414 Artificial Intelligence and COMP9444 Neural Networks and Deep Learning.
Alan's research interests include self-learning for strategic games, robot navigation, language processing, convolutional network architectures and training, adversarial and coevolutionary dynamics, multi-task learning, hierarchical evolution and computational creativity.
Alan is an active member of the artificial intelligence community and has established a number of competitions and conferences, including the Australasian Conference on Artificial Life and Computational Intelligence, IEEE Conference on Computational Intelligence and Games, Australasian Conference on Interactive Entertainment and Robocup Junior at UNSW Sydney.
About UNSW's School of Computer Science and Engineering (CSE)
Welcome to the School of Computer Science and Engineering (CSE), which is also commonly referred to as UNSW Computing. UNSW Computing is part of the Faculty of Engineering. We were founded in 1991 out of the former Department of Computer Science within the School of Electrical Engineering and Computer Science. We are now one of the largest Schools of our kind in Australia.
The academic staff at CSE have focused their research in areas such as Artificial Intelligence, Databases, Embedded and Operating Systems, Networks, Programming Languages, Service Oriented Computing, Software Engineering, Theory, and UNSW Computing is a partner with Data61 (formally NICTA - the National ICT Australia group).
The Faculty of Engineering at UNSW is the largest Engineering faculty in Australia and offers the widest range of Engineering degrees in the country. With eight schools and more than 70 years’ experience, our researchers are at the forefront of exciting technological developments, engaging in partnerships both here and overseas. Our consistently high global academic rankings across Engineering disciplines reflects the depth and breadth of our teaching and research. In the 2021 Times Higher Education rankings, we ranked 1st in Australia for both teaching and research in Engineering and Technology. UNSW Sydney’s study programs have been carefully designed to match global career opportunities.
That’s why Australia’s top employers prefer our graduates over any other university, and why we’re ranked 22nd in the world for employer reputation.
Student Testimonials
"The course was helpful in building my fundamental knowledge around computer science, I was particularly interested in the next generation of automation with the learning capability. Course facilitator Anna was great throughout my studies, answered my questions and responded my exercises promptly, thank you Anna."
Will, Aviation/Commercial Airline
