Course syllabus

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Course memo

The course is offered by the department of Mechanics and Maritime Sciences.

General information

The course will be given only on-site at Chalmers. There will be no remote (Zoom) lectures. More information will follow in the first lecture.

Contact details

During the course, we strive to be available as much as possible. You are welcome to ask questions at any time, e.g. in the lectures (and you may also ask questions via e-mail or telephone). You are always welcome at our offices. You do not need to make an appointment, but since we are not always in our offices it's a good idea to first check that we are there (e.g. via e-mail or telephone).

Lecturer and examiner:

Professor Mattias Wahde, tel.: 031 772 3727, e-mail: mattias.wahde@chalmers.se

Course assistant:

Minerva Suvanto, e-mail: minerva.suvanto@chalmers.se

Finding our offices: Go to Hörsalsvägen 7, enter the building (nya M-huset), so that you have Café Bulten on your right as you enter. Then go up one flight of stairs, and enter the corridor (Vehicle Engineering and Autonomous Systems). If the door is locked, please dial the appropriate number, as shown in the list beside the door.

Course purpose

The aim of the course is for the participants to gain knowledge regarding natural language processing (NLP) and conversational AI. Students will learn about text processing, text classification, large language models (LLMs), and conversational agents, based on either neural and symbolic models. Emphasizing interpretable and accountable AI, the course covers both theoretical aspects and the practical aspects related to NLP and conversational AI. Ethical aspects of conversational AI are also covered.

Schedule

The course schedule is given below. The lectures can also be found in TimeEdit. 

Date Room Time  Content
20250121 HC3 08.00-09.45 Course introduction and motivation; Brief description of the topics covered in the course; Introduction to NLP.
20250122 HA3 13.15-17.00

Text preprocessing (human language and grammar, tokenization, error correction and normalization, part-of-speech tagging, named-entity recognition).
20250128 HC3 08.00-09.45 Introduction to C# and Python (Colab and Jupyter)
20250129 HA3 13.15-17.00 Text classification (classification vs. regression, perceptrons, Bayesian methods, kNN classification), Handout of Assigment 1.
20250204 HC3 08.00-09.45 Statistical language models (i),  (n-gram models, performance evaluation, embeddings)
20250206 HC2 08.00-09.45 Statistical language models (ii), (sequence-to-sequence learning, RNNs, LSTMs, transformers)
20250212 HA3 13.15-17.00 Assignment work session (assistant available as tutor in the classroom)
20250218 HC3 08.00-09.45 Statistical language models (iii) text classification with BERT, LLMs (e.g., ChatGPT), evaluation of LLMs, Handout of Assignment 2.
20250219 HA3 13.15-17.00 Assignment work session (teacher and assistant available as tutor in the classroom), Handin of Assignment 1.
20250220 HC3 13.15-15.00 Statistical language models (iv): LLMs: applications, advantages and disadvantages, capabilities and limitations, comparison with human language acquisition.
20250228 HC3 08.00-09.45 Symbolic models (Eliza, pattern-based models, information-retrieval models, DAISY)
20250304 HC3 08.00-09.45 Interpretability and accountability in NLP, AI ethics 
20250305 HA3 13.15-17.00 Assignment work session (teacher and assistant available as tutor in the classroom)
20250311 HC3 08.00-09.45 Course summary, description of (NLP-related) research in the AAI group, information about master theses, Handin of Assignment 2
20250312 HA2 13.15-17.00 Spare lecture (probably not used).

Course literature

The course literature will consist of a compendium, lecture notes (slides), and links to various scientific papers and web resources. This material will be provided gradually during the course. Note that, due to the large changes in the course (resulting from the rapid development in the field), the compendium from previous years is now outdated. All course material will be provided (free of charge) on the Modules page.

Changes made since the last occasion (2024)

The course material has been updated here and there, reflecting the rapid development in the field of large language models (LLMs). Moreover, the appendices in the compendium (on C#, Python, and Colab) and the sample program code (in C#) have been updated and improved.

Learning outcomes

After completion of the course the student should be able to...

  • Understand and implement text preprocessing of various kinds
  • Understand and use statistical language models (both n-gram models and neural models)
  • Carry out text classification with several different methods.
  • Understand and describe the advantages and disadvantages of LLMs, as well as their limitations
  • Use various prompting techniques in connection with LLMs
  • Describe and compare various different (applications of) LLMs
  • Describe and contrast neural and symbolic models in NLP
  • Describe and discuss interpretable (conversational) AI, and be able to contrast interpretable models with black box models.
  • Describe and discuss the ethical implications of conversational agents.

Examination 

Examination: There will be two assignments, worth a total of 100 p. More details will follow when the course starts.

The dates for handing in the assignments can be found in the schedule above. The detailed requirements will be given in connection with each assignment. In order to pass the course, students must hand in satisfactory solutions to (the mandatory parts of) both assignments. Grades will then be set as follows:

Grade 3: Up to 60p

Grade 4: [61, 80] p

Grade 5: [81-100] p

For GU students, the following applies:

Grade G: Up to 73p

Grade VG: 74p and above

Note: All deadlines are at 23.59.59 on the specified dates, and it is the time when the submission is received that counts. Submissions that are handed in late will receive a lower score, as described in each assignment document. The possibility of submitting assignments will be closed at midnight on 20250331.

Re-examination, grade improvement etc.: It is possible for students to improve their grade by resubmitting assignments. However, after the final submission deadline during the course (20250331) resubmission is only allowed in connection with the re-exam periods in August (2025) and January (2026). 

Note: All submissions must be handed in via the Canvas page. E-mailed submissions will not be considered.

 

 

Course summary:

Date Details Due