Undergraduate AI Core Courses

AI core courese that undergraduate students must take before enroling into the Master of Engineering in AI-Integrated Engineering

To ensure the success in completing the Master thesis, students in the AIEP program must have strong background in mathematics and engineering (through their Engineering track) and also in AI.

Therefore, students must complete the following three courses (or equivalent courses):

  • Applied AI for Engineering (01204162)
  • Mathematical Foundations for AI Engineers (under development)
  • Programming Concepts for Data Processing and Analysis (under development)

The course descriptions are listed below.

Applied AI for Engineering (01204162)

Fundamentals of AI and AI techniques. Basics of machine learning and neural networks. Current AI technology. Data in AI cycles. Applications of AI technology for solving engineering problems. Social impact of AI and ethical issues.

Mathematical Foundations for AI Engineers

Vectors and vector spaces. Matrices and matrix representations of linear systems. Linear transformations. Solution of linear systems. Eigensystems. Singular value decomposition. Derivatives and optimization. Partial derivatives. Gradient descent. Basic statistics. Sampling distribution. Confidence Intervals. Hypothesis testing.

This course is under development

Programming Concepts for Data Processing and Analysis

Basic data structures. Searching and sorting. Recursion. Algorithm complexity. Basic concepts of database systems. Data manipulation and searching. Exploratory data analysis. Data visualization.

This course is under development