COMP 2211 Exploring Artificial Intelligence

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COMP 2211  Exploring Artificial Intelligence 

Course Description 

This course aims to give a gentle introduction to the basic elements of artificial intelligence (AI) through understanding examples from various applications and handson experimentation using AI software tools. In addition to covering the technical aspect of AI through such topics as search and problem solving, knowledge representation, probabilistic reasoning, machine learning, computer vision and image processing, speech and language processing, and robotics, this course will also study the historical perspective, social and ethical implications, as well as potential and limitations of AI. 

List of Topics 

1. Brief history of AI 

2. Search and problem solving 

3. Knowledge representation 

4. Probabilistic reasoning 

5. Machine learning 

6. Computer vision and image processing 

7. Speech and language processing 

8. Robotics 

9. Social and ethical implications of AI 

10. Potential and limitations 

Keyword Syllabus 

1. A brief history of AI 

2. Naïve Bayes 

3. K-nearest neighbors 

4. K-means clustering 

5. Perceptron and multi-layer perceptron 

6. Fundamentals of image processing 

7. Convolutional neural networks 

8. Minimax and alpha-beta pruning 

9. Fundamentals of reinforcement learning 

10. Artificial intelligence ethics 

An innovative approach will be adopted to cover some basic elements of the technical topics through interesting examples. Specifically, the topics will not be covered one-byone separately. Instead, real-world examples that require integrative use of multiple topics will be chosen for illustration. For example, AI for games will be used to illustrate search and problem solving, knowledge representation, and machine learning; AI for autonomous vehicles to illustrate computer vision, machine learning, and robotics; AI for conversational agents (or chatbots) to illustrate speech/language processing and machine learning; AI for healthcare to illustrate image processing and machine learning; etc. 

Reference books 

Hadelin de Ponteves. AI Crash Course: A fun and hands-on introduction to machine learning, reinforcement learning, deep learning, and artificial intelligence with Python. Packt Publishing. 2019. 

Denis Rothman, Matthew Lamons, Rahul Kumar, Abhishek Nagaraja, Amir Ziai, and Ankit Dixit. Python: Beginner’s Guide to Artificial Intelligence: Build applications to intelligently interact with the world around you using Python. Packt Publishing. 2018. 

Prateek Joshi, Artificial Intelligence with Python: Build real-world artificial intelligence applications with Python to intelligently interact with the world around you. Packt Publishing. 2017. 

Sandipan Dey, Python Image Processing Cookbook. Packt Publishing. 2020. 

Grading Scheme 

Laboratory exercises	10%
Programming assignments	30%
Midterm examination	20%
Final examination	40%
Total	100%

Course Intended Learning Outcomes 

1. Demonstrate general understanding of the historical perspective and development of artificial intelligence (AI) 

2. Demonstrate fundamental understanding of the basic elements of AI thinking 

3. Demonstrate proficiency in applying basic principles and techniques of AI and using AI software tools to solve problems in a range of applications 

4. Demonstrate awareness of the social and ethical implications as well as potential and limitations of AI  

Assessment rubrics 

Course Learning Outcome	Exemplary	Competent	Needs Work	Unsatisfactory
1. Demonstrate understanding of the historical perspective and development of artificial intelligence (AI)	Demonstrate thorough understanding of the historical perspective and development of artificial intelligence (AI).	Demonstrate sufficient understanding of the historical perspective and development of artificial intelligence (AI).	Demonstrate insufficient understanding of the historical perspective and development of artificial intelligence (AI).	Is unable to understand the historical perspective and development of artificial intelligence (AI).
2. Demonstrate understanding of the basic elements of AI thinking.	Demonstrate thorough understanding of the basic elements of AI thinking.	Demonstrate sufficient understanding of the basic elements of AI thinking.	Demonstrate insufficient understanding of the basic elements of AI thinking.	Is unable to understand the basic elements of AI thinking.
3. Demonstrate proficiency in applying basic principles and techniques of AI and using AI software tools to solve problems in a range of applications.	Demonstrate thorough understanding of the basic principles and techniques of AI. Is able to use AI software to solve problems in a wide range of applications.	Demonstrate sufficient understanding of the basic principles and techniques of AI. Is able use AI software to solve problems in standard applications.	Demonstrate marginal understanding of the basic principles and techniques of AI. Is able to use AI software to solve simple applications.	Demonstrate little understanding of the basic principles and techniques of AI. Have great difficulty in using AI software even in simple applications.
4. Demonstrate awareness of the social and ethical implications as well as potential and limitations of AI.	Demonstrates a comprehensive awareness of the social and ethical implications as well as potential and limitations of AI.	Demonstrates a thorough awareness of the social and ethical implications as well as potential and limitations of AI.	Demonstrates a basic awareness of the social and ethical implications as well as potential and limitations of AI.	Demonstrates a lack of awareness of the social and ethical implications as well as potential and limitations of AI.