Credits: 5
Schedule: 10.01.2019 - 08.04.2019
Teaching Period (valid 01.08.2018-31.07.2020):
III - IV (Spring)
Learning Outcomes (valid 01.08.2018-31.07.2020):
Artificial intelligence (AI) tackles complex real-world problems, such as question answering, speech recognition, social network analysis, and task scheduling, with rigorous mathematical methods and tools. The goal of this course is to give an in-depth introduction to AI methodology while approaching the topic from the perspective of concrete application problems. Having completed the course, you have gained a comprehensive overview of AI and understand its fundamental principles related to machine learning and logical reasoning. You have excellent premises for solving real-world problems with modern AI techniques and building intelligent systems by implementing such techniques.
Content (valid 01.08.2018-31.07.2020):
The course presents a range of central AI techniques and provides the students with an extensive toolbox for solving problems in practice. For applications that require high degree of adaptation, specific techniques such as (deep) machine learning, reinforcement learning, and graphical models are included. These methods are instrumental for decision under uncertainty. For the purposes of knowledge representation and reasoning, different logical representations such as formulas and rules are covered. These representations establish the foundations for declarative problem solving and enable the use of state-of-the-art solver technology to search for solutions. The course also encourages the students to combine the logical and machine learning perspectives when solving future problems.
Assessment Methods and Criteria (valid 01.08.2018-31.07.2020):
Compulsory programming assignments, tutorial exercises, and exam. The overall course grade depends on the points earned from these sources.
Workload (valid 01.08.2018-31.07.2020):
Lectures, exercise sessions, independent work, and examination.
Study Material (valid 01.08.2018-31.07.2020):
Electronic material made available at MyCourses.
Substitutes for Courses (valid 01.08.2018-31.07.2020):
ICS-E4000 Artificial Intelligence
Prerequisites (valid 01.08.2018-31.07.2020):
Programming skills (CS-A1110 or equivalent), data structures and algorithms (CS-A1140 or equivalent), basics of probability theory (MS-A050* or equivalent) and linear algebra.
Grading Scale (valid 01.08.2018-31.07.2020):
0-5
Details on the schedule (applies in this implementation):
- Practical arrangements, Introduction to AI
- Search methods
- Logical reasoning
- Probabilistic reasoning
- Games
- Logic applications
- Decision making
- Reinforcement learning
- Data mining
- Wrap-up
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