About this paper
| Paper title | Artificial Intelligence |
|---|---|
| Subject | Computer Science |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,141.35 |
| 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, 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:- Search and optimisation algorithms (including genetic algorithms)
- Probabilistic reasoning method (including Bayesian methods)
- Machine learning algorithms (with a focus on neural networks)
- Teaching Arrangements
There are two 1-hour lectures, and one 2-hour lab/tutorial session per week.
- Textbooks
Artificial Intelligence A Modern Approach (Fourth Edition), by Stuart Russell and Peter Norvig, Pearson 2020.
- Course outline
- 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
About this paper
| Paper title | Artificial Intelligence |
|---|---|
| Subject | Computer Science |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,173.30 |
| 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
- 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, 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:- Search and optimisation algorithms (including genetic algorithms)
- Probabilistic reasoning method (including Bayesian methods)
- Machine learning algorithms (with a focus on neural networks)
- Teaching Arrangements
There are two 1-hour lectures, and one 2-hour lab session per week.
- 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