DTE-2501 AI Methods and Applications - 10 stp

This course can be taken as a single subject course.

However, the student is expected to have some basic knowledge of AL and ML, as well as experience in Python programming.

The course targets machine-based problem solving providing a broad range of methods employed in

the field. Topics covered include:

• Supervised learning: Introduction to classification and regression problems eg k-NNs.
• Unsupervised learning: Introduction to clustering problems eg k-Means
• Genetic algorithms: Approaches to swarm intelligence, ant colony optimization, and genetic algorithms will be introduced.
• Natural Language processing methods:
• Dimensionality reduction: Principal component analysis
• Generative models: Gaussian mixture models
• Ensemble techniques: Boosting (DT) and bagging
• Reinforcement learning: MDP , PI and VI, Known world and Unknown world etc
• Dynamic Programming: Travel salesman

General study qualification with Mathematics R1+R2 and Physics FYS1. Application code: 9391

Recommended prerequisites:

• DTE-2602 Introduction to Machine Learning and Artificial Intelligence
• Basic knowledge about Machine Learning and Artificial Intelligence
• Experience in Python programming
• DTE-2510 Introduction to programming
• DTE-2511 Advanced programming

On completion of the course, the successful student is expected to have the following:

Knowledge

The student will have:

• An overview of numerous approaches in artificial intelligence.
• Basic understanding of possibilities and limitations of the approaches presented in the course.
• Basic understanding of the theory and application of the approaches presented in the course.

Skills

The student should be able to:

• Program, adapt and apply AI algorithms on predefined problems.
• Analyze and evaluate results of AI algorithms.
• Think critically with theoretical framework underpinning an ML algorithm.

General Competence

• Can apply the knowledge and skills to solve problems and communicate about the results with other specialists in the field of computer science.

The subject uses so-called "Flipped classroom", i.e., lectures are posted online continuously during the semester in the form of short instructional videos and demonstrations .In addition, exercises and control questions related to each video are used.

The subject teaches in the autumn semester with teacher-led and assistant-led learning and / or exercises.