Applied Artificial Intelligence (Summer University) (Spring 2022)

Abstract

In this course, the students learn how to evaluate, design and prototype products based on latest artificial intelligence technologies.

Description

Artificial intelligence driven technology is becoming a central part of an increasing number of products, ranging from self-driving cars, to home assistants. Being able to design products based on this technology and evaluate their impact is a complex skill that requires knowledge if the inner working of different artificial intelligence algorithms and the ability to experiment with them.

While maintaining a product centric perspective, this course aims at giving the students the ability to understand the different mechanisms at the basis of modern artificial intelligence and use this knowledge to design and evaluate AI based applications.

NB: Please not that you can't take the course if you have already taken Introduction to Artificial Intelligence.

Formal prerequisites

• understanding of basic computing concepts -- e.g. what is a process, what is a file or what is memory
• understanding of fundamental principles of imperative programming -- e.g. variables, functions, parameters
• experience in writing, building and running simple applications written in a scripting language -- e.g. python, processing, javascript

Intended learning outcomes

After the course, the student should be able to:

• formulate hypotheses on implications of artificial intelligence technologies in new products
• analyse the impact of AI on the potential end-user
• describe the mechanisms that drive modern day artificial intelligence
• compare multiple algorithms and identify the most appropriate for a given application
• identify advantages and limitations of artificial intelligence algorithms
• compile simple prototypes based on pre-existing library to experiment with AI methods

Learning activities

The course consists of exercises integrated into lectures with the teacher. The purpose of the lectures is to provide the context of the topics covered and available tools. During the lectures, the students will immediately get practical experience applying the tools to problems relevant to their exam hand-ins. There is a strong emphasis on problems and practices a student might come across in their professional environment.

Topics covered: 

1. Intro to AI theory and ML libraries.
2. Predicting Insurance cost using Regression and Deep Learning.
3. Image classification using Deep Learning.
4. Customer Segmentation with Clustering Methods.
5. Application of Natural Language Processing to sentiment analysis on Twitter.
6. Application of Natural Language Processing to topic modeling.
7. CONV-NET on CIFAR-10/Fashion-MNIST.
8. Intro to RL with application to Moon Lander.
9. Optimization - Genetic Algorithm, Hill Climbing, Evostrat.
10. CIFAR-10 image generation with GANs.
11. AI ethics.

Course literature

The course literature is published in the course page in LearnIT.

Ordinary exam

Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C11: Submission of written work

reexam

Exam type:
C: Submission of written work, External (7-point scale)
Exam variation:
C11: Submission of written work

Time and date