Course syllabus

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EEN095 EEN095 Artificial intelligence and autonomous systems lp1 HT22 (7.5 hp)

The 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. Genetic Algorithms
  3. Decision Trees
  4. Least Square Method Linear
  5. Least Square Methods Non-Linear
  6. Artificial Neural Networks
  7. Reinforcement Learning

Schedule

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Nomenclature:

LXX: Lecture X

EY: Exercise Y

T1 and T2: Tutorial 1 and Tutorial 2

AZ: Assignment and Lab. Session Z (Computer Lab)

DAZ: Due date for Assignment Z

QW: Quizzes (to be delivered before the exercise sessions)

QT1: tutorial quiz (to be delivered before the tutorial session)

Full-Schedule:

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 (2x2 hours per week )[LXX], exercises (2 hours per week)[EY], and home assignments with sessions in a computer lab (2 hours per week)[AZ], including two tutorials (2x2 hours) [T1, T2].

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 [AZ] sessions. Each assignment has a due date defined in the schedule as [DAZ]. At the end of Assignment 3, 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 AZ sessions (see schedule above) 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.
  • Active learning activities are included to support the lecture and exercises.
  • Changes in the evaluation process.

Learning objectives and syllabus

After completion of the course and given a set of basic AI/ML approaches, the students will be able to:

  • define their principal advantages and disadvantages to differentiate them. 
  • classify them according to their application areas to identify how and when to use them. 
  • interpret and implement them using a standard programming language.

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 several parts, both for the Laboratory module and the lecture:

  1. Compulsory Quizzes:
    During the course, there will be a series of short quizzes that you need to complete. The idea of the quizzes is to prepare some background knowledge needed either for the exercises or the tutorials. The quizzes contain a few multiple-choice questions. To complete the quizzes, you have to answer correctly more than 50% of the questions. You will have up to 3 attempts to get this score. 
    NOTE: You will need to complete all the quizzes to pass the Lab.
  2. Compulsory laboratory Assignments (3 Assignments): The goal of these assignments is to provide practical experience in the implementation of basic AI and ML algorithms. The assignments will be delivered in teams (max. 2 participants). 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).  In total, there will be 3 mandatory assignments and 1 assignment with bonus points for the exam. Each assignment will have tasks that can accumulate up to 10 assignment points (ap). There is a strict deadline for delivering each assignment marked as DAZ in the schedule (see above schedule). To pass the laboratory you need to accumulate at least 21 ap, and complete all the quizzes. 
    NOTE: Assignment 4 is not mandatory. However, the collected points in assignment 4 will be counted as bonus points in the exam, e.g., if you collect the 10 ap, you will get 10/100 additional points in the exam.
  3. 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 theory covered in the lectures [LXX], the exercises  [EY], and in the information within the tasks from the assignments [AZ]. In the exam, you will be able to get a total of 100 points. The requirement for passing the exam is:
    Number of Points Exam Grade
    85-100 5
    65-84  4
    45-64 3
    less than 45 fail
    During the exam, the participants are allowed to bring Chalmer's approved calculators and a Formula sheet. Additional material is not allowed.
  4. Bonus Points for the exam: There are two forms of accumulating bonus points for the final exam:
    a) Assignment 4: All the points collected in this assignment will be bonus points for the exam.
    b) Minute-Papers (MP):  We will conduct an active learning activity where you will write a reflection about the topics of the course. In total, there are 4 MP accounting for 4 bonus points in total for the exam.  

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

Course summary:

Date Details Due