AIML 420Artificial Intelligence
This course addresses concepts and techniques of artificial intelligence (AI). It provides a brief overview of AI history and search techniques, as well as covering important machine learning topics, tools, and algorithms with their applications, including neural networks and evolutionary algorithms. Other topics include analysing data, probability and Bayesian networks, planning and scheduling. The course will also give a brief overview of a selection of other current topics in AI.
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Course details
- Dates
- 24 Feb 2025 to 22 Jun 2025
- Starts
- Trimester 1
- Fees
- NZ$1,197.60 for
- International fees
- NZ$5,477.70
- Lecture start times
- Tuesday 12.00pm
- Wednesday 12.00pm
- Thursday 12.00pm
- Campus
- Kelburn
- Estimated workload
- Approximately 150 hours or 9.4 hours per week for 16 weeks
- Points
- 15
Entry restrictions
Taught by
School of Engineering and Computer Science—Faculty of Engineering
About this course
Artificial Intelligence (AI) is intelligence exhibited by machines. Examples include self-driving cars, automatically planning a holiday, generating sensible conversation, learning to predict fog at Wellington Airport, reading a web page to get the answer to a question, recognising handwritten digits, detecting identity by checking fingerprints, detecting network intrusions, controlling robot actuators, processing and recognising images and signals, discovering and detecting the mathematical or logical relationship between output variables and a large number of inputs in economic and engineering tasks, or optimising parameter values in complex engineering problems. AIML 420 is an introduction to the ideas and techniques that computer scientists have developed to address these kinds of tasks.
The lectures cover following main topics: search techniques, machine learning including basic learning concepts and algorithms, neural networks and evolutionary learning, reasoning under uncertainty, planning and scheduling, knowledge based systems and AI Philosophy. The course includes a substantial amount of programming. The course will cover both science and engineering applications.
Course learning objectives
Students who pass this course will be able to:
Explain fundamental concepts and techniques of artificial intelligence, and discuss the applicability and limitations of the algorithms and techniques.
Choose and apply fundamental concepts, techniques and tools of artificial intelligence to real world problems (including engineering applications).
Critically evaluate AI techniques and analysis the results of applying an AI technique to a problem.
How this course is taught
We’ve designed this course for in-person study, and to get the most of out it we strongly recommend you attend lectures on campus. Most assessment items, as well as tutorials/seminars/labs/workshops will only be available in person. Any exceptions for in-person attendance for assessment will be looked at on a case-by-case basis in exceptional circumstances, e.g., through disability services or by approval by the course coordinator. If you started your programme of study remotely and can only study remotely, please contact the School so we can help and confirm what courses are available.
During the trimester there will be typically two lectures and one tutorial per week.
Assessment
- Final Test Type: IndividualMark: 40%
- Assignment 1: Machine learning Basics and Classification Mark: 15%
- Terms test Mark: 15%
- Assignment 2: Neural Networks and Probabilities Mark: 15%
- Assignment 3: Evolutionary computation, Planning and Scheduling Mark: 15%
Assessment dates and extensions
Once you've signed up to this course, you can use to see due dates for assessments and information about extensions.
Mandatory requirements
There are no mandatory requirements for this course.
Lecture times and rooms
What you’ll need to get
You do not need to get any texts or equipment for this course.
Who to contact
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Selected offering
AIML 420
24 Feb–22 Jun 2025
Trimester 1 · CRN 33065