CS511 Advanced Artificial Intelligence

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
CS511 Advanced Artificial Intelligence 3 0 6 Thursday 17:00-19:50
Prerequisite None It is a prerequisite to

None

Lecturer Emine Yaman Office Hours / Room / Phone
Monday:
10:00-13:00
Tuesday:
10:00-12:00
Wednesday:
10:00-13:00
B F2.7C
E-mail eyaman@ius.edu.ba
Assistant Assistant E-mail
Course Objectives The course will cover both basic and advanced artificial intelligence techniques in depth. The course will consist of a mixture of lectures by the instructor and detailed analysis of selected papers by all participants. Each student is also expected to gain hands on experience by carrying out a semester long project on their topic of choice.
Textbook Artificial Intelligence: A Modern Approach (Third Edition), Stuart Russell and Peter Norvig, Prentice Hall, 1994.
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. Provide a strong introduction to learning algorithms in artificial intelligence
  2. Learn how to apply AI algorithm to solve real-life problems
  3. Validate the learning algorithms and publish research papers.
Teaching Methods I will lecture one week and following week each student is expected to be prepared for discussion about related papers. Students are expected to read the required materials and participate in the discussions. Students are also expected to carry-out a semester long project on a problem of their choice.
WEEK TOPIC REFERENCE
Week 1 Introduction to Course Chapter 1
Week 2 Introduction, Intelligent Agents Chapter 2
Week 3 Discussion of Related Papers
Week 4 Solving Problems by Searching Chapter 3
Week 5 Discussion of Related Papers
Week 6 Informed Search and Exploration,Constraint Satisfaction Problems Chapter 4, 5
Week 7 Discussion of Related Papers
Week 8 Adversarial Search, Logical Agents Chapter 6, 7
Week 9 Discussion of Related Papers
Week 10 First Order Logic, Uncertainity Chapter 8, 9
Week 11 Discussion of Related Papers
Week 12 Project Preparation
Week 13 Project Preparation
Week 14 Presentations of Projects
Week 15 Presentations of Projects
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final Project/Presentation 1 60 1,2,3,4,5,6,7,8,9
Semester Evaluation Compenents
Paper Discussion/Presentations 5 40 1,2,3,4,5,6,7,8,9
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture Hours 3 15 45 Preparation for Paper Discussion 9 5 45
Home study 1 15 15 Project 15 3 45
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 12/10/2020
QR Code for https://ecampus.ius.edu.ba/syllabus/cs511-advanced-artificial-intelligence

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