Minor in Artificial Intelligence

After successful completion of this course, students will be able to:

• Explain the foundations, history, and philosophical status of AI.
• Explain the principal ideas in the study of learning and problem solving in deterministic settings.
• Explain the principal ideas in the study of learning and problem solving in uncertain settings.
• Evaluate historical and contemporary views and arguments.
• Detect ethical problems in the application of AI.

Humanity has long been fascinated with the idea of copying the intelligent behavior of living organisms. Since the introduction of programmable computers in the 1950s, the field of artificial intelligence has concerned itself with goals such as creating thinking machines, designing clever software, and building humanoid robots.

Some of the questions we will discuss in this course are: What is computation? Do computers and brains work in a similar way? What kind of problems do intelligent machines need to solve? How do we decide what is artificial intelligence? What kinds of artificial intelligence are there? How do computers learn and solve problems? Is the goal of general artificial intelligence science-fact or science-fiction? How dangerous is AI? How do we design AI that respects our moral intuitions?

We will use a mixture of synchronous and asynchronous activities for learning, including online and offline lectures, discussions, and assignments.

To pass this course, students will need to:

• Submit the assignments.
• Pass the midterm (30% of grade) and the final exam (70% of grade).

• Midterm exam (30%)
• Final exam (70%)

This course takes place in the Fall semester (blocks 1 and 2).

After successful completion of this course, students will be able to:

• Understand and describe fundamental concepts in machine learning (e.g. Regression, Classification and Clustering).
• Describe the most widely used machine learning algorithms, their advantages and shortcomings.
• Apply the most widely used machine learning algorithms on real data using modern Python libraries (e.g. scikit-learn).
• Justify design choices when performing machine learning experiments.

How can we teach computers to identify patterns in data and make decisions on the basis of these patterns? This is the central question in machine learning.  Machine learning is applied in all domains of every day life, from music and film recommendations to financial decisions, security, personalized health care, and practical research. This course provides an introduction to machine learning designed for students of the AI Minor. The course will focus on understanding the fundamental concepts of machine learning and on applying the most common algorithms for machine learning to practical problems using modern Python libraries.

The course will be taught through lectures, as well as practical sessions. The evaluation of the course consists of an individual assignment (30% of the grade) and a final exam (70% of the grade).

• Assignment (30% of the grade).
• Final Exam (70% of the grade).

This course takes place in the Fall semester (blocks 1 and 2).

• Dr. Giacomo Spigler
• Fred Atilla

After successful completion of this course, students will be able to:

• Build computer programs with Python.
• Analyze computational problems and propose logical solutions.
• Turn logical solutions to problems into computer code.
• Define the algorithmic complexity of a solution.

An algorithm is a process that employs a set of predefined rules in order to solve a computational problem within a finite number of steps. Algorithms are used by computers to solve computational problems with speed, reliability and efficiency. However, it is the programmer’s responsibility to write fast, reliable and efficient algorithms. This course will revolve around computational problem solving, discussing both the logical and programming skills required to implement proper algorithms. At the end of the course, you will be able to come up with logical solutions to complex problems and to translate these solutions into computer code.

The course will consist of weekly lectures and practical sessions. The evaluation of the course is a final project, which consists of computer code and a written report. You will work on this project during several practical sessions in the course.

• Individual assignment (40%)
• Final exam (60%)

This course takes place in the Fall semester (blocks 1 and 2).

There are no formal requirements for enrollment in the course. For students without previous programming experience, however, we recommend the following DataCamp skill tracks, which are available for free for Tilburg University students:

• Python Fundamentals
• Python Programming 

Paris Mavromoustakos Blom

The course material will be supplied by the lecturer. Course materials include pdf files, Jupyter notebooks, exercises, and lecture slides.

Yes, it is possible for you to take the AI minor courses. At TSB, the courses may certainly be taken as a minor. Are you a student of another faculty? Then ask your student advisor or exam committee if it is possible to take these courses as a minor in your curriculum. Is this not possible, you are also welcome to take these courses ion top of your study program.

Registration opens in August, more information about registration for courses can be found on this webpage How to register for courses?

Courses are scheduled for semester 1, which runs from September through January (this covers block 1 and block 2). At the end of the summer holidays the schedule will be known and can be found via the schedule. 

All students of Tilburg University can make use, free of charge, of all content on DataCamp. Check the webpage Programming in SQL, R and Python.

The minor is open to internal as well as external students. Some restrictions, however, may apply. Please contact Susanne Warmerdam (study advisor, s.warmerdam@tilburguniversity.edu) for further information

This minor is specifically designed for students without relevant prior knowledge in the field of artificial intelligence. You will only be together with fellow students who have no or little knowledge about these topics. If you have no programming experience at all, we recommend you take 2 specific DataCamp courses to prepare yourself.