Skills for AI

2019-2020

Course Objective

Skills for AI is a brush up course for students with limited background
in 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, and
programming 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 Artificial
Intelligence/Computer Science bachelor degree can attend this course.

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