BSc:IntroductionToArtificialIntelligence old

Introduction to Artificial Intelligence

• Course name: Introduction to Artificial Intelligence
• Course number: XYZ
• Knowledge area: Algorithms and Complexity

Administrative details

• Faculty: Computer Science and Engineering
• Year of instruction: 2nd year of BS
• Semester of instruction: 2nd semester
• No. of Credits: 4 ECTS
• Total workload on average: 144 hours overall
• Frontal lecture hours: 2 per week
• Frontal tutorial hours: 2 per week
• Lab hours: 2 per week
• Individual lab hours: 0
• Frequency: weekly throughout the semester
• Grading mode: letters: A, B, C, D

Prerequisites

• Discrete Math/Logic
• English for Academic Purposes I

Course outline

Have you ever wondered about how computers decide on what your credit worthiness is, or how they can play chess as good as a world master, or how world class circuits can be built with a minimal number of crossed wires? Perhaps you have wanted to build a human like robot, or have wanted to explore the stars with automated probes. Artificial Intelligence is the field which examines such problems. The goal is to provide a diverse theoretical overview of historical and current thought in the realm of Artificial Intelligence, Computational Intelligence, Robotics and Machine Learning Techniques.

Expected learning outcomes

• Understand and apply the PEAS model of problem definition
• Understand and apply the Environment Model
• Understand the role of AI within computer science in a variety of fields and applications
• Gather an appreciation of the history of AI founders
• Solve simple problems using random, guided, and directed, search methods and be able to compare their abilities to solve the problem using a statistical argument
• Apply Evolutionary Algorithms, Neural Networks, Monte Carlo Tree Search to a number of problems

Expected acquired core competences

• Automata
• Compiler design
• Formal methods
• Formal models and semantics
• Formal semantics
• Proof techniques

Textbook

• Russell & Norvig - Artificial Intelligence: A Modern Approach, 3rd Edition
• Ashlock - Evolutionary Computation for Modeling and Optimization

Reference material

NA

Required computer resources

You will need a computer with a C/C++ and LISP compiler

Evaluation

• Assignment 1 (20%)
• Assignment 2 (20%)
• Lab Participation (10%)
• Midterm (25%)
• Final (25%)