Course content

The course focus on data-driven AI topics including machine learning, Bayesian networks, deep learning and we will look at popular and successful applications of these techniques in image processing and natural language processing. Practical applications and real world examples will be carefully walked through so that the students can follow and understand a complete AI project. Lab exercises, and obligatory assignments are important instruments to ensure learning progress with well-defined milestones.

This course replaces the original IMT3104 Artificial Intelligence.

Learning outcome

On successful completion of the module, students will be able to:

* Understand and evaluate various core techniques and algorithms of AI, including regression, machine learning, Markov decision process, and Bayesian networks. Understand the meaning of concepts such as intelligence, classification, clustering and decision-making.

* Identify different uses and applications of AI techniques and algorithms, from neuroscience, understanding brain to image processing, natural language processing, and other types of data different application domains.

* Implement several of the algorithms on different AI problems.

The students will also enhance their programming skills in a preferred language of their own by learning to program AI algorithms.

* Improve programming skills through the programming of AI algorithms. Programming exercises and assignments help enhancing the understanding the theory learnt in class.

* Evaluate the run-time and memory complexity of several AI algorithms, and practice with creating better algorithms.

Learning methods and activities

Lectures, lab exercises, self-study and obligatory assignments.

This course will focus on the practical implementation of AI concepts. Lectures will introduce a topic area, and students are expected to implement and report on the key concept.

Compulsory assignments

• Assignments

Specific conditions

Recommended previous knowledge

• BMA1010 or IMAG2021
• IDATG 2102

Course materials

- History and overview of AI - Bayesian networks - Machine learning - Deep learning - Machine learning for image processing - Natural language processing - Other AI topics