Course syllabus

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Course-PM

MMS131 Introduction to artificial intelligence, SP4, 2024 (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
240318 Mon. 15:15 - 17:00 MDV Course introduction, general overview of course topics. (AIMA, Chapter 1)
240320 Wed. 08:00 – 09:45 MDV Agents and logic. (AIMA, 2, 7.1-7.5.2, 8.1-8.3)
240321 Thu. 10:00 - 11:45 MDV Probabilistic reasoning. (AIMA, 12.1-12.5, 13.1-13.3)
240327 Wed. 08:00 – 09:45 MDV Stochastic optimization methods  (AIMA, 4.1)
240327 Wed. 10:00 – 11:45 DV Exercise class
Break - Easter
240410 Wed. 08:00 – 09:45 MDV Machine learning, Part 1  (AIMA, 19-19.3, 19.7.1)
240411 Thu. 10:00 – 11:45 MDV Machine learning, Part 2  (AIMA, 19.4, 19.9, 12.6, 21.2.2)
240412 Fri. 13:15 – 15:00 DV Exercise class
240417 Wed. 08:00 – 09:45 MDV Natural Language Processing and Conversational AI (AIMA 24.0 24.1, 24.6, 25.1)
240418 Thu. 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)
240419 Fri. 13:15 – 15:00 BM Introduction to machine learning with neural networks. Neural networks - Perceptrons (MLNN, Chapters 1 & 5, and Sections 8.1 & 8.2)
240424 Wed. 08:00 – 09:45 BM Neural networks - Perceptrons (MLNN, Sections 5.1, 5.2, 5.3,  5.5)
240425 Thu. 10:00 – 11:45 BM Neural networks - stochastic gradient descent (MLNN, Sections 6.1 and 6.2 )
240426 Fri. 13:15 – 15:00 BM Neural networks (MLNN, summary of Sections 6.3, 6.4, 6.5, 7.2, 7.3, 7.5, 7.6.1)
240429 Mon. 13:15 – 15:00 BM Recurrent networks (MLNN, Section 9.2)
240506 Mon. 08:00 – 09:45 BM Exercise class (neural networks). Section 8.3 in MLNN and the material I went through in my earlier lectures.
240506 Mon. 10:00 – 11:45 BM Outlook (MLNN, Chapter 10)
240513 Mon. 10:00 – 11:45 MDV Philosophy, Ethics, and Safety of AI (AIMA Chapter 28)
240515 Wed. 08:00 – 09:45 MG Introduction to Reinforcement learning. (MLNN Chapter 11 and/or RLAI, chapters specified under literature.)
240520 Mon. 13:15 – 15:00 MG Reinforcement learning: Summary of basic RL and intro to deep RL
240522 Wed. 08:00 – 09:45 MR/MG

Q&A on Assignment 4.  (Reinforcement learning)

Also on zoom: https://chalmers.zoom.us/j/61500428469
    Password: 832203
240522 Wed. 10:00 – 11:45 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. Examiner is now Marco Della Vedova.

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 and its applications
  • 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 240325 240412 Marco Della Vedova
Assignment 2 30p 240408 240426 Marco Della Vedova
Assignment 3 30p 240424 240513 Berhard Mehlig
Assignment 4 20p 240513 240531 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