### General Information

Course Code | XM_0077 |
---|---|

Credits | 6 EC |

Period | P1 |

Course Level | 400 |

Language of Tuition | English |

Faculty | Faculty of Science |

Course Coordinator | dr. A.C.M. ten Teije |

Examiner | dr. A.C.M. ten Teije |

Teaching Staff |
dr. A.C.M. ten Teije |

### Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods | Study Group, Lecture |
---|

### Course Objective

Skills for AI is a brush up course for students with limited backgroundin linear algebra, logic and programming skills. This courses has three

modules:

Logic-part:

1. express logical statements in propositional and predicate logic

2. reason about the meaning of such formulas through truth tables and

models

3. argue formally whether one formula implies another one

4. reduce a propositional formula to disjunctive or conjunctive normal

form

Linear algebra:

After successfully completing this part,

- the student has a working knowledge of the concepts of matrix

algebra and finite-dimensional linear algebra, such as linear

independence, determinants

- the student is familiar with the general theory of finite

dimensional vector spaces, in particular with the concepts of basis and

dimension

Programming (Python)

After this course the student should be able to write a computer

program in Python, using types (int, boolean, float, list and str),

expressions, assignment statements, if-statements, iterations (while-

and for-statements), using standard functions, using module math,

making functions, and performing I/O, matrices and recursion.

This course contributes to exit qualifications in terms of the Dublin

descriptors "Knowledge and understanding" and "Applying knowledge and

understanding".

### Course Content

This brush up course contains three modules: logic, linear algebra, andprogramming skills.

Logic-part:

The logic part focuses on propositional logic: truth tables, boolean

operators, functional completeness, logical puzzles. In addition the

student will learn the meaning and use formulas of predicate logic, to

express mathematical properties and sentences from natural language.

Linear algebra:

The main topics are: systems of linear equations, linear (in)dependence,

linear transformations and matrices, matrix operations, determinants,

vector spaces and subspaces.

Programming (Python)

During this part, students learn to solve problems using structured

programming. As a side effect, students learn Python, as this is the

programming language in which they practice structured programming.

### Teaching Methods

Lectures, and practical sessions,### Method of Assessment

written exam, practical assignments for programming### Literature

Part 1 Logic: All course materials will be provided via Canvas.Part 2 Linear algebra: All course materials will be provided via

Canvas.

(Linear Algebra and its Applications, by David C. Lay, Steven R. Lay en

Judi J. McDonald, global edition (fifth edition), Pearson.)

Part 3 Programming: An on line book is used (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/english2e/index.html

### Target Audience

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