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COURSE DESCRIPTION AND APPLICATION INFORMATION

Course Name Code Semester T+A+L (hour/week) Type (C / O) Local Credit ECTS
Artificial Intelligence CMPE 470 Fall 03+00+00 Elective 3 5
Academic Unit: Department of Computer Engineering
Mode of Delivery: Face to face
Prerequisites: Basic programming skills. Logic.
Language of Instruction: English
Level of Course Unit: Undergraduate
Course Coordinator: - -
Course Lecturer(s): Habib ŞENOL
Course Objectives: Students will gain understanding on the basics of artifical intelligence. They will learn logic programming and how to apply it to problems related to artifical intelligence. They will solve problems coming from application areas related to artifical intelligence.
Course Contents: Representation of knowledge. Search and heuristic programming. Logic and logic programming. Applications related to problem solving, games and puzzles, expert systems, planning, learning, vision, and natural language understanding.
Learning Outcomes of the Course Unit (LO):
  • 1- To describe a well-defined problem formulation for a complex problem
  • 2- To design intelligent agents
  • 3- To solve well-defined problems using artificial intelligence methods and algorithms
  • 4- To design problem solving agents
  • 5- To develop inference mechanisms and planning capabilities for agents
  • 6- To add learning capabilities to agents
Planned Learning Activities and Teaching Methods: In class lectures and problem solving


WEEKLY SUBJECTS AND RELATED PREPARATIONS

WeekSubjectsRelated Preperation LO
1 Introduction to Artificial Intelligence 1,2
2 Intelligent Agents 1,2
3 Solving Problems by Searching 2,3
4 Informed Search and Exploration 2,3
5 Adversarial Search 3,4
6 Logical Agents 3,4
7 First-order Logic 3,4
8 Inference in First-order Logic 3,4
9 Midterm 1,2,3,4
10 Knowledge Representation 5,6
11 Uncertainty 5,6
12 Probabilistic Reasoning 5,6
13 Learning from Examples 5,6
14 Statistical learning Methods 5,6


REQUIRED AND RECOMMENDED READING

Artificial Intelligence:A Modern Approach, Stuart Russell & Peter Norvig, Prentice-Hall (2010), 3rd Edition


OTHER COURSE RESOURCES

Handouts & Course Slides


ASSESSMENT METHODS AND CRITERIA

Semester RequirementsNumberPercentage of Grade (%)
Homework Assignments 4 25
Final Exam 1 40
Midterms 1 35
Total: 6 100


WORKLOAD

EventsCountDuration (Hours)Total Workload (hour)
Course Hours13452
Homework Assigments41248
Final Exam188
Preparations (out class)2612
Midterms166
Total Workload (hour):126


THE RELATIONSHIP BETWEEN COURSE LEARNING OUTCOMES (LO) AND PROGRAM QUALIFICATIONS (PQ)

# PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 PQ12
LO1                        
LO2                        
LO3                        
LO4                        
LO5                        
LO6