Course syllabus

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EEN095 Artificial intelligence and autonomous systems lp1 HT21 (7.5 hp)

Course is offered by the department of Electrical Engineering

Contact details

Examiner:

Dr. Emmanuel Dean (deane@chalmers.se)

Lecturers:

TAs:

Student Representatives: 

Course purpose

The course aims to provide a basic introduction to Artificial Intelligence (AI) and Machine Learning (ML) methods. Particular emphasis is on applications within robotics.

Course outline

This course consists of a total of 7 topics:

  1. Search
  2. Decision Trees
  3. Reinforcement Learning
  4. Artificial Neural Networks
  5. Trajectory Generation
  6. Genetic Algorithms
  7. Least Square Methods

Schedule

NOTE: This course will follow the hybrid format with online and on-campus sessions (in person). In order to find a safe way for conducting on-campus sessions, we are currently evaluating the options. The place and final schedule are pending the schema approval and will be soon announced.

All the lectures will be held online. Here are the Zoom details:

Zoom link:  https://chalmers.zoom.us/j/61137294638

Password: 968306

 

TimeEdit

Course literature

The following list provides suggested literature for this course. This literature is not mandatory. It is intended to provide additional support for the course. Participants may use it to acquire more detailed information about the topics covered in this course.

[1] Artificial Intelligence: A Modern Approach, S. Jonathan Russell, P. Norvig, Pearson.
[2] Machine Learning. T. M. Mitchell, McGraw-Hill.

Course design

This lecture provides theoretical and practical information to understand and implement basic AI and Machine Learning methods.  The course comprises lectures (2*2 hours per week )[EEN095-L], exercises (2 hours per week)[EEN095-E], and home assignments with Q&A sessions in a computer lab (2 hours per week)[EEN095-QA], including two tutorials (2*2 hours) [EEN095-T].

Each assignment session will cover implementations in the form of practical and programming exercises in Matlab (m-files) and Simulink models. Therefore, the participants will require access to this software. Support for each assignment will be offered in the Q&A sessions. At the end of Assignment 4, we will have a general Q&A session where the participants will be able to revise their acquired knowledge to prepare for the final written exam. 

The main communication will be through the Q&A sessions (see schedule below) and Canvas.

Changes made since the last occasion

  • The syllabus has been modified to fit the goal and scope of the lecture.
  • The course content has been adjusted to fit the new syllabus and for hybrid teaching.
  • The home assignments have been changed since last year (contents and number of assignments). The course workload was revised. The number of assignments was reduced to four.
  • The exercise sessions have been revised to be better aligned with the written exam and home assignments.
  • Each lecture is paired with an exercise session to support the theory covered in them.
  • A mathematical background session is included to introduce the fundamentals needed for this course.

Learning objectives and syllabus

After completion of the course the student should be able to:
    • describe the basic principles in artificial intelligence (AI), including both learning and decision making.
    • analyze and apply learning techniques based on system identification.
    • combine learning and decision-making for both continuous and discrete systems.

Link to the syllabus on Studieportalen.

Study plan

Examination form

Passed a written exam and approved home assignments are required for passing the entire course.

The examination will be divided into two parts, one for the Laboratory module, and the other for the lecture:

  1. Compulsory laboratory Assignments (4 Assignments): The goal of these assignments is to provide practical experience on the implementation of basic AI and ML algorithms. The assignments will be delivered in teams. Each team has to deliver original material for the assignments in the form of m-files and/or Simulink models, depending on the assignment. The code must be accompanied by a short report that describes the delivered solution and how to run it (in the case the delivered material requires custom initialization).  Each assignment will have a set of tasks, which in total can accumulate 10 points. An assignment will be considered as "passed" when it completes at least 6 out of those 10 points. The assignments should be strictly delivered on time according to the course schedule (see below). Any assignment submitted after the deadline will be considered as not delivered (it will be marked as failed). The laboratory will be passed when the participant delivers all four (passed) assignments. 
  2. Compulsory Written Exam: The goal of the written exam is to allow the participants to demonstrate the acquired skills to understand and develop basic AI and ML solutions. The final written exam will be based on the information given in the lectures, exercises, and assignments.  The exam will have a total of 100 points. The requirements for passing the exam is:
    Number of Points Exam Grade
    80-100 5
    60-79  4
    40-59 3
    less than 40 fail
    During the exam, the participants are allowed to bring Chalmer's approved calculators. Another additional material is not allowed.

Further details and information will be given in the first lecture of this course.

EEN095-LX: Lecture X

EEN095-EX: Exercise X

EEN095-QA: Q&A Session X (Computer Lab)

EEN095-DA: Delivery date for Assignment X

Course summary:

Date Details Due