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CIT478Sciences3 Unitsintermediate

Artificial Intelligence

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.

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130h
Study Time
13
Weeks
10h
Per Week
basic
Math Level
Course Keywords
Artificial IntelligenceSearch AlgorithmsKnowledge RepresentationExpert SystemsRobotics

Course Overview

Everything you need to know about this course

Course Difficulty

Intermediate Level
Builds on foundational knowledge
65%
intermediate
➕
Math Level
Basic Math
📖
Learning Type
Theoretical Focus

Course Topics

Key areas covered in this course

1

Definition of Artificial Intelligence

2

Intelligent Agents

3

Search Algorithms

4

Knowledge Representation

5

Programming Languages for AI

6

Natural Language Processing

7

Expert Systems

8

Robotics

Total Topics8 topics

Requirements

Knowledge and skills recommended for success

Basic programming skills

Introductory computer science concepts

💡 Don't have all requirements? Don't worry! Many students successfully complete this course with basic preparation and dedication.

Assessment Methods

How your progress will be evaluated (3 methods)

assignments

Comprehensive evaluation of course material understanding

Written Assessment

tutor-marked assessments

Comprehensive evaluation of course material understanding

Written Assessment

final examination

Comprehensive evaluation of course material understanding

Written Assessment

Career Opportunities

Explore the career paths this course opens up for you

AI Engineer

Apply your skills in this growing field

Data Scientist

Apply your skills in this growing field

Robotics Engineer

Apply your skills in this growing field

NLP Engineer

Apply your skills in this growing field

Knowledge Engineer

Apply your skills in this growing field

Industry Applications

Real-world sectors where you can apply your knowledge

HealthcareFinanceManufacturingAerospaceAutomotive

Study Schedule Beta

A structured 13-week journey through the course content

Week
1

Module 1: Introduction to AI

2h

Unit 1: What Is Artificial Intelligent (AI)?

2 study 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
2

Module 1: Introduction to AI

2h

Unit 2: Introduction to Intelligent Agent (IA)

2 study 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
3

Module 2: Search in Artificial Intelligence

2h

Unit 1: Introduction to State Space Search

2 study hours
  • •Describe state space representation.
  • •Describe algorithms.
  • •Formulate state space search problems.
  • •Analyze algorithm properties.
  • •Identify suitable search strategies.
  • •Solve simple problems.
Week
4

Module 2: Search in Artificial Intelligence

2h

Unit 2: Uninformed Search

2 study hours
  • •Explain uninformed search.
  • •List types of uninformed search.
  • •Describe depth-first and breadth-first search.
  • •Solve problems on uninformed search.
Week
5

Module 2: Search in Artificial Intelligence

2h

Unit 3: Informed Search Strategies

2 study hours
  • •Explain informed search.
  • •Describe best-first search and greedy search.
  • •Solve problems on informed search.
Week
6

Module 2: Search in Artificial Intelligence

2h

Unit 4: Tree Search

2 study hours
  • •Describe a game tree.
  • •Describe two-player games search algorithms.
  • •Explain intelligent backtracking.
  • •Solve problems on tree search.
Week
7

Module 3: Artificial Intelligence Techniques in Programming and Natural Languages

2h

Unit 1: Knowledge Representation

2 study hours
  • •Explain knowledge representation.
  • •Describe the history of knowledge representation and reasoning.
  • •List characteristics of KR.
  • •List features of KR language.
Week
8

Module 3: Artificial Intelligence Techniques in Programming and Natural Languages

2h

Unit 2: Programming Languages for Artificial Intelligence

2 study hours
  • •Describe the history of IPL.
  • •Discuss similarities between Lisp and Prolog.
  • •List areas where Lisp can be used.
Week
9

Module 3: Artificial Intelligence Techniques in Programming and Natural Languages

2h

Unit 3: Natural Language Processing

2 study hours
  • •Describe the history of natural language processing.
  • •List major tasks in NLP.
  • •Mention different types of evaluation of NPL.
Week
10

Module 4: Artificial Intelligence and Its Applications

2h

Unit 1: Expert System

2 study hours
  • •Explain an expert system.
  • •Distinguish between expert systems and traditional problem-solving programs.
  • •Explain the term 'Knowledge Base'.
Week
11

Module 4: Artificial Intelligence and Its Applications

2h

Unit 2: Robotics

2 study hours
  • •Explain the word robotics.
  • •List types of robotics.
  • •Describe the history of robotics.
Week
12

Final Revision

4h

Final Revision and Assignments

4 study hours
  • •Review all modules and units.
  • •Work on assignments and TMAs.
Week
13

Exam Preparation

4h

Final Revision and Exam Preparation

4 study hours
  • •Complete all assignments and TMAs.
  • •Prepare for final examinations.

This study schedule is in beta and may not be accurate. Please use it as a guide and consult the course outline for the most accurate information.

Course PDF Material

Read the complete course material as provided by NOUN.

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Study Tips & Exam Preparation

Expert tips to help you succeed in this course

1

Review definitions and examples of AI concepts from Unit 1 to establish a strong foundation.

2

Create concept maps linking search algorithms (Units 4-7) to their applications and trade-offs.

3

Practice solving search problems from the TMAs using different algorithms to compare their performance.

4

Focus on understanding the syntax and semantics of Lisp and Prolog (Units 8-9) and write simple programs.

5

Study the components and architecture of expert systems (Unit 10) and robotics (Unit 11) to understand their practical applications.

6

Review all TMAs and address any areas where you struggled to reinforce your understanding.

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