It is highly recommended to know: fundamentals of probability theory, and university level calculus.
With the integration of smart devices and systems in human life, it comes the need for intelligent decisions based on the huge data streaming through sensors (e.g., IoT) as well as other sources of technical and nontechnical information. Intelligence includes the capability to learn from data. The intention is to find hidden structure and recognize regular patterns that represent certain relations. Machine learning topic includes (massive) data classification, clustering and projection. The learning is an accumulated process, in the sense that more data my carry more information and henec more sharp knowledge about the process. Learning algorithms lead to accurate prediction about the future and also provide rules for the decision makers in autonomous systems.
The aim of this course is to introduce the foundations of machine learning algorithms with more concentration on the practical applications. The students who successfully pass this course will be able to understand the concepts of machine learning and also several standard learning algorithms. Furthermore, they will be able to write simulation codes to solve some real problems with machine learning. The applications of machine learning in this course may cover vast areas such as: pattern recognition, data mining, robotics, smart automation, cyber-security, bioinformatics and e-health etc.
Course develops lifelong learning, Oral, written and interpersonal skills (Group Work, English), critical and analytical thinking, problem modeling and solving skills, IT skills and optimized decisions.
Data modelling with different statistical regression approaches, parameter modeling and estimation techniques, Bayesian decision theory approach, data classification and clustering algorithms, Principal component analysis approach, Decision trees, Hidden Markov Models approach, Reinforcement learning, Neural networks and Applications.
1. Lecturer Notes
2. E. Alpaydin: Introduction to Machine Learning, 3rd Edition, MIT Press, 2014
3. S. Rogers and M. Girolami, "A First Course in Machine Learning", 2nd Edition, CRC Press 2017
Lectures 32 h, independent work 103 h
Quizzes, exam and simulation projects
Grading: On scale 1-5 or fail
Responsible Person: Prof. Mohammed Elmusrati
Teacher(s): Prof. Mohammed Elmusrati
Responsible Unit: School of Technology and Innovations