Course syllabus

Jump to today

Course-PM

EEN095 EEN095 Artificial intelligence and autonomous systems lp1 HT23 (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 (GA)
  3. Least Square Method Linear (LSML)
  4. Least Square Methods Non-Linear (LSMNL)
  5. Artificial Neural Networks (ANN)
  6. Reinforcement Learning (RL)

Schedule

schedule2023.png

Nomenclature:

LX: Lecture X, e.g. L1 (Lecture 1)

EX: Exercise X, e.g., E2 (Exercise 2)

TX: Tutorial X, e.g., T3 (Tutorial 3)

AX: Assignment and Lab. Session X (Computer Exercises), e.g., A3 (Assignment and Computer Exercise 3)

DAX: Due date for Assignment X, e.g., DA3 (Due date for Assignment 3)

QX: Quizzes, e.g., Q2 (Quizz 2)

MP: Minute-paper activity

MM: Mentimeter activity

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 )[LX], exercises (2 hours per week)[EX], and home assignments with computer exercises (2 hours per week)[AX], including three tutorials (3x2 hours) [T1, T2, and T3].

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 [AX] sessions. Each assignment has a due date defined in the schedule as [DAX]. 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 AX sessions (see schedule above) and Canvas.

Changes made since the last occasion

  • The topic list has been revised and adapted.
  • Changes in the evaluation grades.

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 quizzes aim to prepare some background knowledge needed for the exercises or the tutorials. The quizzes contain a few multiple-choice questions. To complete the quizzes, you must correctly answer 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. The quizzes have strict due dates. Therefore, late submissions will not be accepted.
  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 optional 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 DAX 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 [LX], the exercises  [EX], and the information within the tasks from the assignments [AX]. 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
    84-100 5
    67-83  4
    50-66 3
    less than 50 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