Overview
Modern AI representation systems and problem-solving techniques with a particular emphasis on machine learning.
About this paper
| Paper title | Advanced Artificial Intelligence |
|---|---|
| Subject | Artificial Intelligence |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,627.83 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Restriction
- COSC 343
- Notes
- Students with limited programming experience should also take AIML 401
- Contact
- Teaching staff
- Paper Structure
In this paper we will focus on the hard problems to be solved in artificial intelligence (AI), 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 methods (including Bayesian methods)
- Machine learning algorithms (with a focus on neural networks and deep learning)
- Teaching Arrangements
2x 1h Lectures per week
1x 2h Lab sessions per week
3x 2h tutorial
- 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
View more information about Otago's graduate attributes.- Learning Outcomes
Students who successfully complete the paper will:
- Gain a practical understanding of a selection of core concepts in AI research: planning and search, probabilistic reasoning, machine learning, decision trees, neural networks, sequential decisions, and deep learning.
- Strengthen their understanding of each core concept through practical exercises and experience with implemented systems.
Timetable
Overview
Modern AI representation systems and problem-solving techniques with a particular emphasis on machine learning.
About this paper
| Paper title | Advanced Artificial Intelligence |
|---|---|
| Subject | Artificial Intelligence |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Summer School (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2027 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Restriction
- COSC 343
- Notes
- Students with limited programming experience should also take AIML 401
- Contact
- Teaching staff
Lecturer: Dr Lech Szymanski
- Paper Structure
In this paper, we will focus on the hard problems to be solved in artificial intelligence (AI), 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 methods (including Bayesian methods)
- Machine learning algorithms (with a focus on neural networks and deep learning)
- Teaching Arrangements
Two 1-hour lectures per week
One 2-hour lab session per week
Three 2-hour tutorial
- Textbooks
Artificial Intelligence: A Modern Approach (Fourth Edition), by Stuart Russell and Peter Norvig, Pearson 2020.
- Course outline
- Graduate Attributes Emphasised
- Scholarship, Communication.
View more information about Otago's graduate attributes. - Learning Outcomes
Students who successfully complete the paper will:
- Gain a practical understanding of a selection of core concepts in AI research: planning and search, probabilistic reasoning, machine learning, decision trees, neural networks, sequential decisions and deep learning.
- Strengthen their understanding of each core concept through practical exercises and experience with implemented systems.