INTRODUCTION TO ARTIFICIAL INTELLIGENCE
2017/2018, Semester 2
School of Computing (Computer Science)
Modular Credits: 4
The course is aimed at mid-advanced undergraduate students, as well as beginner graduate students.
The objective of this class is to offer students a comprehensive introduction to the emerging field of artificial intelligence, with an emphasis on its computational, mathematical and economic foundations. In more detail, we will study the following topics
1. What is AI?
2. The design of rational agents
3. Search problems (in particular, uninformed, informed and adversarial search)
4. Constraint Satisfaction Problems
5. Logical agents, first order logic and inference
6. Uncertainty modeling and Bayesian networks
7. An introduction to machine learning
We will cover key chapters from Russel and Norvig's "Artificial Intelligence - A Modern Approach" (3rd Edition).
The course will take a theoretical and mathematical perspective; as such, students are expected to have a solid understanding of algorithms (CS2010 or equivalent) and discrete mathematics (CS1231 or equivalent). A good knowledge of probability and statistics is recommended as well.
By the end of this class, students will be familiar with
1. the foundational concepts of artificial intelligence
2. basic agent design principles
3. the fundamental properties of various search algorithms
4. agent logic
5. Bayesian networks
6. Machine learning basics
Our course will cover key chapters from Russel and Norvig's "Artificial Intelligence - A Modern Approach" (3rd Edition). Copies are available at the central library.
We will cover
1. Chapters 1 & 2: Intro to AI/Agents
2. Chapters 3 - 6: Search
3. Chapters 7 - 9: Logic
4. Chapters 13 & 14: Uncertainty
5. Chapter 18: Machine Learning
This list is subject to change, and may be updated as the course unfolds.
(CS2010 or CS2020 or CS2040 or CS2040C) and (CS1231 or MA1100).
Our class will have one weekly lecture and one tutorial per week. I encourage students to actively participate in both.
Students are assessed on the following:
- 5% tutorials and attendance
- 25% term project
- 20% midterm exam
- 50% final exam
EEE and CPE students can only take this module as a technical elective to satisfy the program requirements or UEM but not CFM/ULR-Breadth.
Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week