Course syllabus

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MMS131 Introduction to artificial intelligence, SP4, 2025 (7.5 hp)

General information

The course is offered jointly by the department of Mechanics and Maritime Sciences (M2) and the Physics department (F). Lectures will be given on-site at the campus, they will not be live streamed nor recorded. We will upload slides after the lectures so that anyone who is ill can still obtain the material from the lecture. However, students who are not ill should attend the lectures on campus.

Contact details

Examiner:

Lecturers:

Teaching assistants:

Course purpose

The course aims to introduce the students to artificial intelligence including, but not limited to, the important subfield of machine learning.

Schedule

The course schedule is given below and can also be found at TimeEdit.

Literature (see section below):

  • AIMA = Artificial intelligence: A modern approach
  • MLNN = Machine learning with neural networks
  • RLAI = Reinforcement learning: An introduction
Date Time Teacher Content
250324 Mon. 08:00 – 09:45 MDV Course introduction, general overview of course topics. (AIMA, Chapter 1)
250326 Wed. 10:00 – 11:45 MDV Agents and logic. (AIMA, 2, 7.1-7.5.2, 8.1-8.3)
250326 Wed. 13:15 – 15:00 MDV Probabilistic reasoning. (AIMA, 12.1-12.5, 13.1-13.3)
250331 Mon. 08:00 – 09:45 MDV Stochastic optimization methods (AIMA, 4.1)
250402 Wed. 10:00 – 11:45 MDV Robotics - perception and action (AIMA 26.1, 26.2, 26.4, 26.5, 3.4.2, 3.5.1, 3.5.2)
250402 Wed. 13:15 – 15:00 DV+VL Exercise class (Tutorial 1)
250407 Mon. 08:00 – 09:45 MDV Machine learning, Part 1 (AIMA, 19-19.3, 19.7.1)
250409 Wed. 10:00 – 11:45 MDV Machine learning, Part 2 (AIMA, 19.4, 19.9, 12.6, 21.2.2)
250409 Wed. 13:15 – 15:00 DV+VL Exercise class (Tutorial 2)
Break
250423 Wed. 10:00 – 11:45 BM Introduction to machine learning with neural networks. Overview.
(MLNN, Chapters 1 & 5, and Sections 8.1 & 8.2 and Slides).
250423 Wed. 13:15 – 15:00 BM Neural networks - Perceptrons (MLNN, Sections 5.1, 5.2, 5.3,  5.5)
250430 Wed. 10:00 – 11:45 BM Neural networks - stochastic gradient descent (MLNN, Sections 6.1 and 6.2 )
250505 Mon. 08:00 – 09:45 BM Neural networks (MLNN, summary of Sections 6.3, 6.4, 6.5, 7.2, 7.3, 7.5, 7.6.1)
250507 Wed. 10:00 – 11:45 BM Recurrent networks (MLNN, Section 9.2)
250507 Wed. 13:15 – 15:00 BM Unsupervised learning (MLNN, Chapter 10)
250509 Fri. 13:15 – 15:00 BM Exercise class (neural networks)
250512 Mon. 15:15 – 17:00 MDV Natural Language Processing and Conversational AI (AIMA 24.0 24.1, 24.6, 25.1)
250514 Wed. 13:15 – 15:00 MG Introduction to Reinforcement learning. (MLNN Chapter 11 and/or RLAI, chapters specified under literature.)
250519 Mon. 08:00 – 09:45 MG Reinforcement learning: Summary of basic RL and intro to deep RL
250519 Mon. 10:00 – 11:45 MR Q&A on Assignment 4.  (Reinforcement learning)
250521 Wed. 10:00 – 11:45 MDV Philosophy, Ethics, and Safety of AI (AIMA Chapter 28)
250521 Wed. 13:15 – 15:00 MDV

Course summary

Course literature

[AIMA] Russell, S.J., Norvig, P., Artificial intelligence: A modern approach (4th ed.)
Selected chapters; see the schedule above. Note: previous editions may be used by students (see the comparison table).

[MLNN] Mehlig, B.,  Machine learning with neural networks. An introduction for scientists and engineers, (2021).  Errata.

  • Chapter 1( introduction),
  • Chapter 5 (perceptrons),
  • Chapter 6 (stochastic gradient descent),
  • Chapter 7 (deep learning),
  • Chapter 8 (convolutional networks),
  • Chapter 11 (reinforcement learning).

[RLAI] Sutton, R. S. & Barto, A. G., Reinforcement Learning: An Introduction (2nd ed.)
Read at least Chapter 1 (Introduction), Chapter 3 (Finite Markov Decision Processes), and Chapter 6 (Temporal-Difference Learning) up to and including 6.5. Chapter 16 (Applications and Case Studies) is also recommended for discussions on some of the early and recent high-profile application.

For some lectures, additional material will be provided (free of charge) and will be uploaded on the course web page.

Slides of the lectures (see Modules).

Course design

The course consists of a sequence of lectures, usually two per week (but note that there are some exceptions; see the schedule above) as well as (usually) one exercise session per week.

The examination will be based on assignments; see below.

Changes made since the last occasion

No significant changes in topics.

Learning objectives and syllabus

Learning objectives:

  • Describe, implement, and apply logic and probabilistic reasoning.
  • Describe, implement, and use various methods for classification, machine inference, clustering, planning, and decision-making
  • Describe and discuss conversational agents (human-machine dialogue)
  • Describe, implement, and use neural networks and deep learning
  • Describe, implement, and use reinforcement learning
  • Describe, implement, and use stochastic optimization algorithms
  • Analyse and critically discuss ethical aspects of AI, including equality, diversity, and inclusion.
  • Discuss and analyze various applications of AI

Study plan.

Examination form

The examination will consist of four home problems, which the students should solve individually. There will be no final exam. The
maximum number of points, the release dates, and the deadlines for the assignments will be as follows:

Assignment n. Points Hand-out (release) Hand-in (deadline) Responsible
Assignment 1 20p 250326 250408 Marco Della Vedova
Assignment 2 30p 250408 250429 Marco Della Vedova
Assignment 3 30p 250428 250513 Berhard Mehlig
Assignment 4 20p 250514 250527 Mats Granath

 

Penalties for delays and deductions in case of incomplete or erroneous solutions will be given separately in the text of each assignment.

Grades will be set as follows:

  • Grade 5: 80p -
  • Grade 4: 60-79p
  • Grade 3: 40-59p

Note that a minimum of 40p is required for a passing grade.

Course summary:

Date Details Due