Course syllabus

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DAT565 / DIT407 DAT565 / DIT407 Introduction to data science and AI lp3 VT25 (7.5 hp)

Course is offered by the department of Computer Science and Engineering

Contact details

Examiner: Senior lecturer Matti Karppa <karppa@chalmers.se>

Lecturers

Other course staff

Postdoc Shuai Wang <shuaiwa@chalmers.se>

Doctoral students:

Student TAs:

Intended learning outcomes

On successful completion of the course the student will be able to:
Knowledge and understanding
  • describe fundamental types of problems and main approaches in data science and AI
  • give examples of data science and AI applications from different contexts 
  • give examples of how stochastic models and machine learning (ML) are applied in data science and AI
  • explain basic concepts in classical AI, and the relationship between logical and data driven, ML-based approaches within AI.
  • briefly explain the historical development of AI, what is possible today and discuss possible future development.
Skills and abilities
  • use appropriate programming libraries and techniques to implement basic transformations, visualizations and analyses of example data 
  • identify appropriate types of analysis problems for some concrete data science applications 
  • implement some types of stochastic models and apply them in data science and AI applications
  • implement and/or use AI-tools for search, planning and problem solving
  • apply simple machine learning methods implemented in a standard library
Judgement and approach
  • justify which type of statistical method is applicable for the most common types of experiments in data science applications 
  • discuss advantages and drawbacks of different types of approaches and models within data science and AI.
  • reflect on inherent limitations of data science methods and how the misuse of statistical techniques can lead to dubious conclusions 
  • critically analyze and discuss data science and AI applications with respect to ethics, privacy and societal impact
  • show a reflective attitude in all learning

Course content

During the course, a wide selection of methods for Data Science and AI will be introduced. The course is divided into three parts:

Introduction to data science

  • Implementation of data science solutions, using Python, basic data analysis and visualization.
  • Introduction of the data science process, and appropriate methodology.
  • Examples of core data science methods with case studies such as in clustering, classification and regression.
  • Data science put in context regarding ethics, regulations and limitations.

Statistical methods for data science and AI

  • Introduction of some common stochastic models with examples of applications in data science and AI (for instance, naive Bayes classifiers, topic models for text and Hidden Markov Models for sequence data).

Artificial Intelligence

  • Introduction to classical AI and machine learning, including the relationship to related areas such as algorithms and optimization, and AI philosophy.
  • Examples of methods and applications of AI, in classical AI (search and constraint satisfaction), and ML-based (search engines, naive Bayes and neural networks)
  • Discussion of ethics and societal impact of AI.

Study plan (Chalmers)

Syllabus (GU)

Schedule

The course is organized as follows:

  • Lectures on Tue and Fri at 13:15–15 (GD-salen)
  • Self-directed labs three times a week at varying locations and times (check TimeEdit!)

TimeEdit

Course literature

Steven S. Skiena: "The Data Science Design Manual". 2017. Springer. Available through Chalmers network and library at https://link.springer.com/book/10.1007/978-3-319-55444-0

Course design

The course has eight mandatory assignments. There is always two weeks to work on one assignment, and up to two assignments will run on parallel (e.g., first assignment for weeks 1 and 2, with deadline at the end of week 2, second assignment for weeks 2 and 3, and so on).

Assignment deadlines are hard and no extensions are given. You will receive feedback for the assignments one week after the deadline, based on the version that was submitted by the deadline.

Most assignments are returned as Jupyter notebooks on CodeGrade. Some parts of the assignment will be automatically graded, which means you will get immediate feedback for those parts. Assignments may also be multiple-choice quizzes on Canvas.

Assignments are done in pairs and you need to select your group within CodeGrade.

In order to pass the course, you must pass all assignments. An assignment is considered a pass if you have obtained at least 70% of the maximum score.

There is no mandatory attendance on either lectures or lab sessions. Lab sessions consist of independent work on the assignments and there will be course staff available for help.

There is reading attached for each lecture. You are supposed to read that before the lecture. The lectures are structured under the assumption that you have read the text in the course textbook, and the content of the textbook will not be repeated. Instead, the lectures complement the textbook and offer further examples, proofs, details etc.

There will be a Slack that can be used to contact the lecturer and course staff outside lecture and lab hours, and ask for help or give suggestions.

Changes made since the last occasion

CodeGrade has been introduced and is being piloted for a large-scale course. The system automatically grades technical aspects of the reports that combine text, images, and code, in the form of a Jupyter Notebook.

Examination form

The course is graded PASS/FAIL (G/U).

There is no particular exam. Instead, the course is considered PASS when one has passed all eight mandatory assignments. Assignments are done in groups of two students.

Course summary:

Date Details Due