This course covers the foundations of artificial intelligence as a holistic discipline. Artificial intelligence (AI) is the intelligence exhibited by machines or software. Artificial intelligence covers the many aspects of how human intelligence is encoded in computer programs and mechanisms such as robots. This course introduces the foundation of simulating or creating intelligence from a computational point of view. It allows the students to gain generic problem-solving skills that have applicability to a wide range of real-world complex problems. It covers the techniques of reduction, reasoning, problem solving, search, knowledge representation, and machine learning. It also covers computational complexity and issues arising at the junction between biological and artificial intelligence.

Machine learning is a complex yet a fast-moving field with many real world commercial applications. The goal of machine learning is to build computer models that can produce useful information whether they are predictions, associations, or classifications. The ultimate goal for many machine-learning researchers is to build computing systems that can automatically adapt and learn from their experience. This course will study the theory and practical algorithms, basic concepts and paradigms, key techniques, challenges, and tricks of machine learning. It also covers examples of how machine learning is used/applied today in the real world, and exposes students to the construction and use of machine learning algorithms. This course discusses recent applications of machine learning, such as robotic control, speech recognition, face recognition, data mining, autonomous navigation, bioinformatics, and text and web data processing. It also fuses machine learning with other areas of artificial intelligence and robotics.

This course explores the computational processes and artificial intelligence basis of robotics. The integration of software and hardware systems will be emphasized through proper computational paradigms such as algorithms, automata, search structures, and data manipulation in real-time reactive systems. Coverage of electronics and electronic interfaces will provide a solid foundation on which to base artificial intelligence structures. The use of sensors and motors, as controlled by software will be covered in addition to the use of embedded and mechanical software driven systems. A special emphasis shall be placed on robot autonomy and learning through the precise use of computer algorithms and data structures. Robot sensing, analyzing, vision, and locomotion through computational structures will also be covered.

This course will provide an advanced study of the latest research and applications in artificial intelligence, machine learning, robotics, and the data science used in their applications. It will survey complex and relevant issues and will provide students with a holistic look into the advanced concepts of AI, which fuse together many areas of computer science, mathematics, and engineering. The course concludes with a comprehensive research paper that covers new and emerging areas of the fields of AI, machine learning, and robotics.

This course covers advanced robotics computing areas such as robotics programming and robot operating systems. It applies the concepts of artificial intelligence and machine learning with electrical and mechanical structures to produce functioning robots that are logically and structurally sound in both hardware and software. The course is hands-on and robots will be constructed and programmed to perform various computationally complex tasks including navigation, sensing, effecting and actuating. The course concludes with the construction of a robot that is thoroughly analyzed and tested.