In the course Computational Intelligence, we will focus mainly on computational aspects of Artificial Intelligence, namely, optimization algorithms for solving learning problems. Specifically, we will consider problems that cannot be solved using information about gradient due to their combinatorial character or complexity of the objective function (e.g., non-differentiability, blackbox objective function). These problems pop up in computer science and AI, such as, identification of biological systems, task scheduling on chips, robotics, finding optimal architecture of neural networks. For this purpose, we will introduce different classes of algorithms that can be used to tackle these problems, namely, hill climbing and local search, and evolutionary algorithms. Additionally, we explain sampling methods (Markov Chain Monte Carlo) and population-based sampling methods, and indicate how they are linked to evolutionary algorithms. In the second part of the course, we will discuss neural networks as current state-of-the-art modeling paradigm. We will present basic components of deep learning, such as, different layers (e.g., linear layers, convolutional layers, pooling layers, recurrent layers), non-linear activation functions (e.g., sigmoid, ReLU), and how to use them for specific problems. At the end of the course, we will touch upon alternative approaches to learning using Reinforcement Learning. We will conclude the course with a recently revived field of neuroevolution that aims for utilizing evolutionary algorithms in training neural networks.

Contact hours listed under a course description may vary due to the combination of lecture-based and independent work required for each course therefore, CEA's recommended credits are based on the ECTS credits assigned by VU Amsterdam. 1 ECTS equals 28 contact hours assigned by VU Amsterdam.