Artificial Intelligence
- Sciences
- 400 level
- 3 credit units
- 191 pages
- 11 units
This course introduces students to the fundamental concepts of Artificial Intelligence (AI). It explores various AI approaches, including intelligent agents and different search algorithms like state space, informed, and uninformed searches. The course also covers knowledge representation techniques, programming languages commonly used in AI (Lisp, Prolog), and natural language processing. Finally, it delves into AI applications such as expert systems and robotics, providing a comprehensive overview of the field.
About this course
- Difficulty
- Intermediate
- Study hours
- 130 hours
- Maths
- Basic
- Content
- Theoretical, practical, case study
- Practical work
- No
- Basic programming skills
- Introductory computer science concepts
- Assignments
- Tutor marked assessments
- Final examination
What you'll read
The real module and unit structure of CIT478, taken from the course material NOUN publishes.
One paragraph, so you can see how it reads
CIT478 · UNIT 1: WHAT IS ARTIFICIAL INTELLIGENT (AI)?
The fourth view of AI is that it is the study of rational agents. This view deals with building machines that act rationally. The focus is on how the system acts and performs, and not so much on the reasoning process. A rational agent is one that acts rationally, that is, is in the best possible manner.
What you should be able to do
- Define and explain the core concepts of Artificial Intelligence.
- Describe and differentiate various search algorithms used in AI.
- Apply knowledge representation techniques to model real-world problems.
- Explain the principles and applications of natural language processing.
- Design and implement simple expert systems for specific domains.
- Describe the components and applications of robotics.
What it prepares you for
- AI Engineer
- Data Scientist
- Robotics Engineer
- NLP Engineer
- Knowledge Engineer
- Healthcare
- Finance
- Manufacturing
- Aerospace
- Automotive
- Lisp
- Prolog
Where it gets hard
The units students slow down on, and what makes each one heavy.
- Module 2: Search in Artificial Intelligence
Unit 3: Informed Search Strategies
Informed search strategies require understanding of heuristics and their impact on search efficiency, which can be challenging for beginners.
- Module 3: Artificial Intelligence Techniques in Programming and Natural Languages
Unit 1: Knowledge Representation
Knowledge representation involves understanding different formalisms and their trade-offs, requiring abstract thinking and logical reasoning.
A suggested way through it
13 weeks, about 30 hours in total. Yours will differ.
- Week 1Module 1: Introduction to AI
Unit 1: What Is Artificial Intelligent (AI)? · 2 hours
Read the definition of AI and its branches.. Identify the faculties involved with intelligent behavior.. Understand different approaches to AI.. Study example systems that use AI.. Review the history of AI..
- Week 2Module 1: Introduction to AI
Unit 2: Introduction to Intelligent Agent (IA) · 2 hours
Explain what an agent is and how it interacts with the environment.. Identify percepts and actions in a problem situation.. Measure agent performance.. List state-based agents.. Identify environment characteristics..
- Week 3Module 2: Search in Artificial Intelligence
Unit 1: Introduction to State Space Search · 2 hours
Describe state space representation.. Describe algorithms.. Formulate state space search problems.. Analyze algorithm properties.. Identify suitable search strategies.. Solve simple problems..
- Week 4Module 2: Search in Artificial Intelligence
Unit 2: Uninformed Search · 2 hours
Explain uninformed search.. List types of uninformed search.. Describe depth-first and breadth-first search.. Solve problems on uninformed search..
- Week 5Module 2: Search in Artificial Intelligence
Unit 3: Informed Search Strategies · 2 hours
Explain informed search.. Describe best-first search and greedy search.. Solve problems on informed search..
- Week 6Module 2: Search in Artificial Intelligence
Unit 4: Tree Search · 2 hours
Describe a game tree.. Describe two-player games search algorithms.. Explain intelligent backtracking.. Solve problems on tree search..
- Week 7Module 3: Artificial Intelligence Techniques in Programming and Natural Languages
Unit 1: Knowledge Representation · 2 hours
Explain knowledge representation.. Describe the history of knowledge representation and reasoning.. List characteristics of KR.. List features of KR language..
- Week 8Module 3: Artificial Intelligence Techniques in Programming and Natural Languages
Unit 2: Programming Languages for Artificial Intelligence · 2 hours
Describe the history of IPL.. Discuss similarities between Lisp and Prolog.. List areas where Lisp can be used..
- Week 9Module 3: Artificial Intelligence Techniques in Programming and Natural Languages
Unit 3: Natural Language Processing · 2 hours
Describe the history of natural language processing.. List major tasks in NLP.. Mention different types of evaluation of NPL..
- Week 10Module 4: Artificial Intelligence and Its Applications
Unit 1: Expert System · 2 hours
Explain an expert system.. Distinguish between expert systems and traditional problem-solving programs.. Explain the term 'Knowledge Base'..
- Week 11Module 4: Artificial Intelligence and Its Applications
Unit 2: Robotics · 2 hours
Explain the word robotics.. List types of robotics.. Describe the history of robotics..
- Week 12Final Revision
Final Revision and Assignments · 4 hours
Review all modules and units.. Work on assignments and TMAs..
- Week 13Exam Preparation
Final Revision and Exam Preparation · 4 hours
Complete all assignments and TMAs.. Prepare for final examinations..
Preparing for the exam
- Review definitions and examples of AI concepts from Unit 1 to establish a strong foundation.
- Create concept maps linking search algorithms (Units 4-7) to their applications and trade-offs.
- Practice solving search problems from the TMAs using different algorithms to compare their performance.
- Focus on understanding the syntax and semantics of Lisp and Prolog (Units 8-9) and write simple programs.
- Study the components and architecture of expert systems (Unit 10) and robotics (Unit 11) to understand their practical applications.
- Review all TMAs and address any areas where you struggled to reinforce your understanding.
Questions students ask about this course
What is CIT478 about?
This course introduces students to the fundamental concepts of Artificial Intelligence (AI). It explores various AI approaches, including intelligent agents and different search algorithms like state space, informed, and uninformed searches. The course also covers knowledge representation techniques, programming languages commonly used in AI (Lisp, Prolog), and natural language processing. Finally, it delves into AI applications such as expert systems and robotics, providing a comprehensive overview of the field.
How many units does CIT478 have?
CIT478, Artificial Intelligence, has 11 units across 4 modules, over 191 pages of course material. You can read it one unit at a time.
How many credit units is CIT478?
CIT478 carries 3 credit units, at 400 level in Sciences.
Is CIT478 hard?
CIT478 is rated intermediate level, with basic mathematical content. It is mostly theoretical, practical and case study work.
How long does CIT478 take to study?
About 130 hours of study, spread across its 11 units.
How is CIT478 assessed?
CIT478 is assessed by assignments, tutor marked assessments and final examination.
What do I need before starting CIT478?
Basic programming skills Introductory computer science concepts
What can I do with CIT478?
AI Engineer, Data Scientist, Robotics Engineer, NLP Engineer and Knowledge Engineer.