Skills for AI

Course Objective

Skills for AI is a brush up course for students with limited background in mathematical skills necessary for understanding Artificial Intelligence: linear algebra, logic and probability.

The goals are:

• Familiarise students with logical and mathematical language (Knowledge and understanding)
  
• Let students use logic and linear algebra to express AI applications (Knowledge and understanding)
• Let students solve mathematical exercises, both manually and with the help of the computer in Python (Applying knowledge and understanding)

Course Content

This brush up course contains the following topics: logic, linear algebra, probability theory, and gradients and derivatives.

Logic-part:
The logic part focuses on propositional logic and first order predicate logic:

• For propositional logic, the following is covered: truth tables, boolean operators, semantic equivalence, rewriting rules, functional completeness.
  
• For first order logic, students will learn predicates, quantifiers, logic models, reasoning rules and converting natural language to first order logic.

Linear algebra:
The main topics are: systems of linear equations, linear (in)dependence, linear transformations and matrices, matrix operations, determinants and eigen vectors. The theory will be used to model computer science and AI problems such as computer graphics, language recognition, path finding and neural networks.

For probability theory: events, probability, conditional probability and Bayes' Theorem will be covered.

Gradients and derivative: Definition and interpretation of the derivative as a gradient, computing derivatives for basic polynomial functions.

Additional Information Teaching Methods

Method of Assessment

The course will be examined in the following way:

• Quizzes after each lecture on canvas. These determines 20% of the grade
• Assignments will be made in small groups of 2-4 students (making report using a given dataset and open questions. mostly python based): These determines 30% of the grade
• A final written exam at the end of the course. This determines 50% of the grade A minimum grade of 5.5 for each component is required.
  

A resit option for the final written exam will be offered. If a student misses of fails one project assignment, one assignment can be resubmitted in the week after the final written exam.

Literature

Part 1 Logic: All course materials will be provided via Canvas.

Part 2 Linear algebra: All course materials will be provided via Canvas.
Optionally, students can use the book: (Linear Algebra and its Applications, by David C. Lay, Steven R. Lay en Judi J. McDonald, global edition (fifth edition), Pearson.)

Students may wish to consult: (How to Think Like a Computer Scientist, Learning with Python, 2nd Edition, by Jeffrey Elkner, Allen B. Downey, and Chris Meyers) see the URL:
http://openbookproject.net/thinkcs/python/english3e/index.html

Additional Information Target Audience

Only first year master Artificial Intelligence students without an Artificial Intelligence/Computer Science bachelor degree can attend this course.

Recommended background knowledge

Some programming experience with python is necessary and jupyter notebooks is recommended.