Due to COVID-19 restrictions, a selection of on-campus papers will be made available via distance and online learning for eligible students.
Find out which papers are available and how to apply on our COVID-19 website
An introduction to modern AI representation systems and problem-solving techniques.
| Paper title | Artificial Intelligence |
|---|---|
| Paper code | COSC343 |
| Subject | Computer Science |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 1 (On campus) |
| Domestic Tuition Fees (NZD) | $1,092.15 |
| International Tuition Fees (NZD) | $5,004.75 |
- Prerequisite
- COSC 201 or COSC 242
- Recommended Preparation
- COSC 202
- Schedule C
- Arts and Music, Science
- Contact
Computer Science Adviser, adviser@cs.otago.ac.nz
- More information link
- View more information about COSC 343
- Teaching staff
- Lecturer: Dr Lech Szymanski
- Paper Structure
In this paper we will look at some different definitions of intelligence and at the concept of intelligent agents. As a way of focusing on the hard problems to be solved in AI, we will do some practical work with LEGO robots, concentrating on the issue of how to get information about the world and how to make use of it. We will consider techniques for machine learning and probabilistic reasoning. Almost every human ability results from learning from experience: we will look at how these learning processes can be modelled computationally.
Topics to be considered include:- Embodied AI and robots
- Search and optimisation algorithms (including genetic algorithms)
- Probabilistic reasoning method (including Bayesian methods)
- Machine learning algorithms (with a focus on neural networks)
Assessment:
- Two assignments 40%
- Final exam 60%
- Teaching Arrangements
There are two lectures per week, plus weekly lab/tutorial sessions.
- Textbooks
Artificial Intelligence A Modern Approach (Third Edition), by Stuart Russell and Peter Norvig, Prentice Hall 2010.
- Course outline
- View the course outline for COSC 343
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Scholarship, Communication, Ethics, Teamwork.
View more information about Otago's graduate attributes. - Learning Outcomes
Students who successfully complete this paper will gain an understanding of a selection of core concepts in AI research: autonomous agents, planning and search, probabilistic reasoning,machine learning, decision trees, neural networks and sequential decisions.
- In each case, this understanding will be strengthened through practical exercises and experience with implemented systems
- The paper will also give students an awareness of the increasing influence of these technologies in daily life
Timetable
An introduction to modern AI representation systems and problem-solving techniques.
| Paper title | Artificial Intelligence |
|---|---|
| Paper code | COSC343 |
| Subject | Computer Science |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2022 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- COSC 201 or COSC 242
- Recommended Preparation
- COSC 202
- Schedule C
- Arts and Music, Science
- Contact
Computer Science Adviser, adviser@cs.otago.ac.nz
- More information link
- View more information about COSC 343
- Teaching staff
- Lecturer: Dr Lech Szymanski
- Paper Structure
In this paper we will look at some different definitions of intelligence and at the concept of intelligent agents. As a way of focusing on the hard problems to be solved in AI, we will do practical work on appropriate systems (for example, LEGO robots have been used in the past), concentrating on the issue of how to get information about the world and how to make use of it. We will consider techniques for machine learning and probabilistic reasoning. Almost every human ability results from learning from experience: we will look at how these learning processes can be modelled computationally.
Topics to be considered include:- Embodied AI and robots
- Search and optimisation algorithms (including genetic algorithms)
- Probabilistic reasoning method (including Bayesian methods)
- Machine learning algorithms (with a focus on neural networks)
Assessment:
- Internal assessment (assignments) 40%
- Final exam 60%
- Teaching Arrangements
There are two lectures per week, plus weekly lab/tutorial sessions.
- Textbooks
Artificial Intelligence A Modern Approach (Fourth Edition), by Stuart Russell and Peter Norvig, Pearson 2020.
- Course outline
- View the course outline for COSC 343
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Scholarship, Communication, Ethics, Teamwork.
View more information about Otago's graduate attributes. - Learning Outcomes
Students who successfully complete this paper will gain an understanding of a selection of core concepts in AI research: autonomous agents, planning and search, probabilistic reasoning,machine learning, decision trees, neural networks and sequential decisions.
- In each case, this understanding will be strengthened through practical exercises and experience with implemented systems
- The paper will also give students an awareness of the increasing influence of these technologies in daily life
