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ID1214 Artificial Intelligence and Applied Methods 7,5 hp

Course memo Autumn 2024-50184

Version 2 – 10/09/2024, 6:58:38 PM

Course offering

Autumn 2024-50184 (Start date 28 Oct 2024, English)

Language Of Instruction

English

Offered By

EECS/Computer Science

Course memo Autumn 2024

Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2024

Content and learning outcomes

Course contents

The following fields are treated within the scope of the course:

  • Fundamental AI problems and solutions including search algorithms and planning, knowledge representation forms and knowledge including reasoning strategies, decision support and heuristics.
  • Intelligent agents and multi-agent systems
  • Automatic analysis and generation of natural language.
  • Machine learning and neural networks.

Focus is on artificial intelligence for knowledge-based systems, agent system and strategies.

Intended learning outcomes

After passing the course, the students should be able to:

  • give an account of artificial intelligence and its application areas
  • know and account for artificial intelligence methods and technologies
  • formulate and carry out a well delimited and qualified assignment that applies artificial intelligence techniques.

Learning activities

The focus is on Artificial Intelligence with AI concepts, AI areas, AI techniques, AI algorithms, and applying these to AI problems.

Learning Objectives

For grade E, the student must be able to:

  • describe Artificial Intelligence (AI) and its applications
  • describe AI concepts
  • describe and apply AI areas
  • describe and apply AI techniques
  • describe and apply AI algorithms and AI methods
  • implement AI techniques and AI algorithms to solve limited problems
  • in cooperation with another student devise and solve a sophisticated but well-delimited problem, involving an AI area, an AI technique, and AI algorithm and an AI method
  • reflect on others AI material as well as own produced material.

For a higher grade, the student must furthermore be able to create a detailed description of an individual solution to a sophisticated problem, by applying an AI technique, motivate the choice of AI area, and compare the selected area and technique with other existing, related AI methods and AI techniques. These skills must be demonstrated via a written exam.

To learn the different AI areas the course is divided into modules with lectures, tasks, assignment, seminars, laboratories, and quizzes.

Lectures

Most of the lectures will be given physically. Please check the calendar for each lecture to make sure that these are given either in Electrum, Kista or online via Zoom. The lectures are not mandatory but they provide vital information that is useful for the tasks, labs, and assignments.

Commonly lectures are not recorded! However, when recorded lectures, these will be available via Canvas.

The lectures present the following topics:

  • Introduction to AI and its applications, vision, and future
  • Searching, planning, and scheduling
  • Intelligent rules-based systems, Decision-support systems, expert systems, and knowledge-based systems including reasoning strategies and heuristics, software development
  • Software agents, intelligent agents, and multi-agent systems
  • Machine learning
  • Neural networks and deep learning
  • Natural language processing

Tasks (Pass / Fail)

For each AI topic, see lectures above, there is a task of writing a short description of the AI area and providing five questions together with answers.

Peer-review (Pass / Fail)

For the AI topics, peer-reviews of the other students' descriptions of the AI area will be made after the descriptions have been handed in.

Seminars

The seminars follow the lectures. During the seminars, the current AI area is discussed with techniques and problems. Also, the questions and answers, that the students have provided for the task of the AI area, are discussed. The lectures, seminars with questions, answers, and tasks shall prepare the students for the quizzes. For tasks and quizzes, see below. The seminars are not mandatory but they provide answers to questions that the students may have about the areas, tasks, and the main assignment.

Laboratories (Pass / Fail)

The laboratory consists of practical programming tasks. These tasks are for imparting theoretical knowledge.

Quizzes (Pass / Fail)

Online quizzes are provided for each topic. The quizzes contain:

1) closed-questions with answer alternatives and

2) open questions.

The open question can only be answered after the closed questions are correctly answered. The number of tests for the 1) closed questions with answer alternatives is unlimited whereas the 2) the open question can only be answered once.

  • Correct answers on both quizzes lead to a pass on this part.
  • A failure in any of the quizzes leads to fail on this part and requires to retake this part during the written exam.*)

*) To clarify this part: the written exam includes one part where questions about the different AI topics are asked (that is the AI topics presented during the course), see the topics in the lecture section above.

The student only needs to take the questions for the AI topics that failed during the course. When this is done, the student will receive the grade E. However, to get a higher grade the second part of the written exam must be taken, for more information see the written exam below.

Main project

Course participants will undertake a main project, performed in groups of two students. In the main project, the students must choose an AI area, and implement a solution using an AI technique. The project must be novel, and either different from any existing work or significantly extending an existing work.

The project is presented at a seminar at the end of the course. At that time, the students give an oral presentation (with slides), and demonstrate the execution of a working prototype. Consequently, the prototype must work, and must give results that are reasonable and expected.

In case the presentation or demonstration fails, the students will have two weeks to improve the prototype, after which time the students give an improved presentation and demonstration. Further improvements have, up to now, never been necessary.

Written Exam (A-F)

The written exam includes two parts:

  • the first part with all the AI topics that are presented during the course, see lectures above. The part is graded with a pass or fail and gives an E on the written exam. Observe that this part only needs to be taken, if and only if, any, some, or all of the quizzes for the different AI tasks failed. The students only take the questions that correspond to the failed quizzes. 
  • the second part is about an AI problem to be solved. The solution is to provide and, in detail, describe the select AI technique and tool(s), that must be correct for solving the particular AI problem.

Grading

Main Project (INL1), 4 credits, mandatory

Examination (TEN1), 3.5 credits, including the following (for E=mandatory and Higher grade):

  • Assignments (marked pass or fail), mandatory
  • Lab work (marked pass or fail), mandatory
  • Peer-review (marked pass or fail), mandatory
  • On-line quizzes (marked pass or fail), mandatory
  • Optional written exam for a higher grade than E (marked fail/E, D, C, B, or A)

All mandatory parts must be passed for a final grade. A student that has passed all mandatory parts, but not the optional written exam, will get grade E.

CHATGPT or other GPTs

Under no circumstance is CHATGPT or other GPTs allowed during this AI course. If students are asked to compare with the outcome of CHATGPT or another GPT, they will get this assignment under the tasks. Otherwise, the use of CHATGPT or other GPTs will be considered cheating and/or misleadning and will be reported. 

Detailed plan

 

Learning activities Content Preparations

Lectures

Tasks

Seminars

Labs

Introduction to AI, vision, and future

Chapter 1: Introduction and Chapter 10: AI in society – Revolution and Future

Håkansson & Hartung: ARTIFICIAL INTELLIGENCE Concepts, Areas, Techniques and Applications (Studentlitteratur, 2020)

+ online publications

+ suggestions of other books

 

Searching, planning, and scheduling

Chapter 2: Searching, planning and scheduling

Håkansson & Hartung: ARTIFICIAL INTELLIGENCE Concepts, Areas, Techniques and Applications (Studentlitteratur, 2020)

+ online publications

+ suggestions of other books

  Decision-support system, expert systems, and knowledge-based systems, software development

Chapter 3: Decision support systems, expert systems and knowledge-based systems

Håkansson & Hartung: ARTIFICIAL INTELLIGENCE Concepts, Areas, Techniques and Applications (Studentlitteratur, 2020)

+ online publications

+ suggestions of other books

  Software agents, intelligent agents, and multi-agent systems

Chapter 4: Agent-based systems and multi-agent systems

Håkansson & Hartung: ARTIFICIAL INTELLIGENCE Concepts, Areas, Techniques and Applications (Studentlitteratur, 2020)

+ online publications

+ suggestions of other books

  Machine learning

Chapter 5: Machine learning

Håkansson & Hartung: ARTIFICIAL INTELLIGENCE Concepts, Areas, Techniques and Applications (Studentlitteratur, 2020)

+ online publications

+ suggestions of other books

 

Neural networks and deep learning

Chapter 6: Neural networks and Chapter 7: Deep Learning

Håkansson & Hartung: ARTIFICIAL INTELLIGENCE Concepts, Areas, Techniques and Applications (Studentlitteratur, 2020)

+ online publications

+ suggestions of other books

  Natural language processing

Chapter 8: Natural language processing

Håkansson & Hartung: ARTIFICIAL INTELLIGENCE Concepts, Areas, Techniques and Applications (Studentlitteratur, 2020)

+ online publications

+ suggestions of other books

  Miscellaneous topics and tools

Chapter 9: Natural language processing

Håkansson & Hartung: ARTIFICIAL INTELLIGENCE Concepts, Areas, Techniques and Applications (Studentlitteratur, 2020)

+ online publications

+ suggestions of other books

Main project

Solve an AI problem together with one other student.

Select a topic, and provide a detailed description and a program, solving the AI task.

See suggestions of topics in Canvas.


Schema HT-2024 - change the link !

Preparations before course start

Literature

HÅKANSSON A., HARTUNG, R.L., 2020: ARTIFICIAL INTELLIGENCE Concepts, Areas, Techniques and Applications, Studentlitteratur, Lund. (main literature)

NORVIG, P., RUSSEL, S., 2021: Artificial Intelligence: A Modern Approach. Pearson Education Limited; 4th edition. (complementing book)

+ Articles and papers, and other recommended literature

Equipment

Own laptop

Software

Programming language, well-known to the student, such as:

Python

Java

Prolog

But also other programming tools, such as:

Other programming languages, tools and libraries, for example:

VisiRule

GAMA

WEKA

Tensorflow

Keras

Examination and completion

Grading scale

A, B, C, D, E, FX, F

Examination

  • INL1 - Written assignment, 4.0 credits, Grading scale: P, F
  • TEN1 - Examination, 3.5 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Written examination. Written assignment that is reported in groups.

The section below is not retrieved from the course syllabus:

Written assignment ( INL1 )

Individual tasks for the different AI-topics presented during the course, see lectures. This is graded with pass/fail.

Labs for each AI topic, individually or in a student group of two students. The students must demonstrate a well-functioning program. This is graded with pass/fail.

Two-students main project solving a particular AI problem. This task includes a written report describing the problem and solution, and well-functioning software.

Examination ( TEN1 )

There are two quizzes for each AI topic. Students answer these quizzes, individually. Correct answers to all questions on all quizzes for all AI topics results in the grade E on TEN1.

A written exam with two parts (can improve the grade on the course, from E, see quizzes above):

  • one part for the AI-topcis where the students that have failed the quizzes must take, graded pass / fail.
  • second part with a particular AI problem to be solved, grade A-F.

Grading criteria/assessment criteria

Individual tasks, labs, individual quizzes, and the main project:

All tasks (task 1 - task 7) are graded with a pass or fail.

All labs are graded with a pass or fail.

All quizzes are graded with a pass or fail.

The main project is graded with a pass or fail. However, an excellent project with high-quality programming solving an very difficult task can increase the final grade on the course. Hence, students that are between two grades can get a higher grade thanks to the main project.

 

Opportunity to complete the requirements via supplementary examination

If any or all of the quizzes are graded with fail, the students can take the written exam, with the two parts.

Opportunity to raise an approved grade via renewed examination

A second written exam can be taken, that can increase or decreas the final grade.

Alternatives to missed activities or tasks

Missed any of the tasks, labs and two-part quizzes for each AI-topics:

- one part of the written exam is about the AI-topics. The students that have failed any of the quizzes during the course, can take the questions that concern the AI-topic(s) that the student failed earlier.

Failed tasks and labs must be corrected and handed in via Canvas.

All parts need to be finalized before the course ends. Activities outside the course frame will not be corrected.

Reporting of exam results

Results are in Canvas and Ladok.

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

No information inserted

Round Facts

Start date

28 Oct 2024

Course offering

  • Autumn 2024-50184

Language Of Instruction

English

Offered By

EECS/Computer Science

Contacts

Course Coordinator

Teachers

Teacher Assistants

Examiner