Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
LEARNING OUTCOMES
The students has an overview of various applications of AI and of the fundamental problems, methods, and algorithms that underlie and that are necessary to build AI applications. The course gives the student a good basis for further study of the said methods and algorithms, with the main goal of 'demystifying' AI.
Credits: 3
Schedule: 02.03.2021 - 09.04.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Arno Solin
Teacher in charge (applies in this implementation): Arno Solin
Contact information for the course (valid 01.03.2021-21.12.2112):
For practical questions, please contact the Head Teaching Assistant Dr. William Wilkinson (william.wilkinson@aalto.fi). Include the course code"CS-C1000" in the message subject.
The lecturer can be reached at arno.solin@aalto.fi, but for practical matters, see above.
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
Valid 01.08.2020-31.07.2022:
The course consists of lectures introducing applications of AI and the underlying computational problems as well as describe the principles of the methods and algorithms that are used to solve problem. The focus is on demystifying AI and giving the student and overview of the field. The course contains problem sets on the topics of the lectures. The content of the lectures may vary yearly.
Applies in this implementation:
This course is intended as a primer in artificial
intelligence (AI). The course goes through basic concepts (with
examples) in AI, covering topics in symbolic AI, data mining, and
machine learning. The overall goal is demystifying these
concepts and giving the students a basic understanding about the
past, the present, and a bit about the future of AI. This course is
intended as a non-technical introduction, which means that prior skills
in programming or mathematics are not required.After the course, the student has an
understanding about basic concepts in AI, ML. The student should
understand the setup behind common AI systems, and know some of the
possibilities and limitations they have.
Assessment Methods and Criteria
Valid 01.08.2020-31.07.2022:
Following the lectures and solving/returning assignments.
Applies in this implementation:
Assessment based on lecture quizzes, essay, and computer exercise
demos. No physical presence is required.
Workload
Valid 01.08.2020-31.07.2022:
Lectures c. 20 h, assignments related to course content c. 60 h, totaling c. 80 h.
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
Lecture notes and other given material.
Applies in this implementation:
Lecture material, exercise notebooks, additional reading material.
Prerequisites
Valid 01.08.2020-31.07.2022:
Introductory programming course recommended, not required.
SDG: Sustainable Development Goals
9 Industry, Innovation and Infrastructure
FURTHER INFORMATION
Details on the schedule
Applies in this implementation:
- Lectures on Fridays (starting March 5)
- Exercise sessions on Tuesdays
Only exception on Easter week. Check MyCourses Lectures/Assignments sections for details.
- Lectures on Fridays (starting March 5)
- Teacher: Chang Paul
- Teacher: Hou Yuxin
- Teacher: Solin Arno
- Teacher: Trapp Martin
- Teacher: Wilkinson William