Course syllabus

Course memo

MMS131 Introduction to Artificial Intelligence, SP4, 2022 (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. 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

During the course, we strive to be available as much as possible. You are welcome to ask questions at any time, either in the lectures or via e-mail or telephone. You are always welcome at our offices, but it's a good idea to first check (e.g. via e-mail or telephone) that the person you intend to visit is in the office.

Examiner:

Professor Mattias Wahde (MW), Tel: 772 3727, e-mail: mattias.wahde@chalmers.se

Lecturers:

Professor Mattias Wahde (MW), Tel: 772 3727, e-mail: mattias.wahde@chalmers.se

Professor Bernhard Mehlig (BM), e-mail: bernhard.mehlig@physics.gu.se

Senior Lecturer Mats Granath (MG): Tel: 0723087160, e-mail: mats.granath@physics.gu.se

Teaching assistants:

Wengang Mao (WM), e-mail: wengang.mao@chalmers.se

Basudha Srivastava (BS), e-mail: basudha.srivastava@physics.gu.se 

Ludvig Storm (LS), ludvig.storm@physics.gu.se

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

AIMA = Artificial intelligence: A modern approach, 3rd (note!) Edition (see Literature below)

MLNN = Machine learning with neural networks (see Literature below and Errata)

RLAI = Reinforcement learning: An introduction (see Literature below)

Date	Time	Room	Teacher	Content
20220321	08.00-09.45	HB4	MW	Course introduction, general overview of course topics
20220324	08.00-09.45	HB4	MW	Agents and logic. AIMA, Chapters 2, 7-8 (2.1-2.5, 7.1-7.5.2, 8.1-8.3, 8.5)
20220325	13.15-15.00	HB3	MW	Probabilistic reasoning. AIMA, Chapters 13-14 (13.1-13.5,13.7,14.1-14.2, 14.4, 14.8), Hand-out of Assignment 1
20220328	08.00-09.45	HB4	WM	Exercise class (related to the lecture on 20220324) Demonstrated problems:  2.1, 2.11 (a-d), 7.2, 7.4 (c-e), 7.17 (a-c), 8.9 (a-d), 8.20 (a-b)  Recommended problems (for students): 7.1, 7.4 (a-b,f-k), 7.7 (a-c), 8.9 (e-g), 8.26 (a-f)
20220331	08.00-09.45	HB4	MW	Clustering, classification, and decision-making, Part 1 (AIMA, (parts of) Chapters 16-18, as well as separate lecture notes)
20220401	13.15-15.00	HB3	MW	Clustering, classification, and decision-making, Part 2 (AIMA, (parts of) Chapter 18 as well as separate lecture notes)
20220404	08.00-09.45	HB4	WM	Exercise class (Related to the lectures on 20220325, 20220331) Demonstrated problems: 13.4, 13.7, 13.15, 14.8 (a-d), 16.5, 16.17, 17.18 Recommended problems (for students): 13.8, 13.10a,b, 13.13, 14.1, 14.12a-c, 16.1, 16.3, 16.7, 16.12
20220407	08.00-09.45	HB4	MW	Stochastic optimization methods (AIMA, Chapters 3-4, as well as separate lecture notes), Hand-out of Assignment 2.
20220408	13.15-15.00	HA1	MW	Conversational AI (separate lecture notes), Hand-in of Assignment 1
20220421	08.00-09.45	HB4	BM	Introduction to machine learning with neural networks Neural networks - Perceptrons (MLNN, Chapters 1 & 5)
20220422	13.15-15.00	HB4	BM	Neural networks - Perceptrons (MLNN, Chapters 5 & 6)
20220425	08.00-09.45	HA1	BM	Neural networks - stochastic gradient descent (MLNN, Chapters 6 & 7 ) Hand-out of Assignment 3 (stochastic gradient descent)
20220428	08.00-09.45	HB4	TBA	Neural networks -  Convolutional networks  (MLNN, Chapter 8)  Hand-in of Assignment 2
20220429	13.15-15.00	HB4	BM, LS	Exercise class (neural networks)
20220502	08.00-09.45	HB4	MG	Introduction to Reinforcement learning. (MLNN Chapter 11 and/or RLAI, chapters specified under literature.) Lecture notes for both RL lectures
20220505	08.00-09.45	HB4	MG	Reinforcement learning: Summary of basic RL and intro to deep RL, Hand-out of Assignment 4
20220506	23.59			Hand-in of Assignment 3
20220509	15.15-17.00	Zoom	BS	Q&A on Assignment 4.  (Reinforcement learning),  Zoom link: https://gu-se.zoom.us/j/66589390093
20220512	08.00-09.45	HB4	MW	Robotics - perception and action (AIMA Chapters 24-25)
20220513	13.15-15.00	HA1	MW	Ethics in artificial intelligence (separate lecture notes), Hand-in of Assignment 4
20220516	08.00-09.45	HB4	MW	Course summary.
20220519	08.00-09.45	HB4	BM	Outlook: machine learning with neural networks (MLNN, Chapter 8)

 

Course literature

[1] Russell, S.J., Norvig, P. Artificial intelligence: A modern approach, 3rd (note!) Edition  (AIMA) (Selected chapters; see the schedule above). Note: There is now a 4th edition. Preferably use the 3rd edition (some chapter numbers have changed, for example)

[2] Mehlig, Bernhard, 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).

[3] Sutton, Richard S. & Barto, Andrew 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 (under the module "Additional literature")

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.

Learning objectives and syllabus

Learning objectives:

After completing the course, the students should be able to

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 for the assignments will be as follows:

Assignment 1: 15p

Assignment 2: 35p

Assignment 3: 30p

Assignment 4: 20p

The hand-out and hand-in dates can be found in the schedule above. 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.