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This course provides an introduction to the field of machine learning, focusing on the fundamental concepts, techniques, and applications of this rapidly growing field.

The course begins with an overview of the basic principles of machine learning, including supervised and unsupervised learning, regression, classification, clustering, and reinforcement learning. It then covers key topics such as model selection, feature selection, regularization, optimization, and evaluation.

The course also covers advanced topics such as deep learning, natural language processing, and computer vision. Additionally, students will learn about the ethical and social implications of machine learning, including bias, fairness, and transparency.

The course will include a combination of lectures, hands-on exercises, and assignments that will provide students with practical experience in applying machine learning algorithms to real-world problems. By the end of the course, students will have a strong understanding of the key concepts and techniques of machine learning and will be able to apply them to solve real-world problems.

Prerequisites: Students should have a solid background in mathematics and statistics, as well as programming experience in a high-level language such as Python. Familiarity with linear algebra, calculus, and probability theory is recommended.

Course Goals:

• Understand the basic principles of machine learning, including supervised and unsupervised learning, regression, classification, clustering, and reinforcement learning.
• Gain practical experience in applying machine learning algorithms to real-world problems.
• Understand the ethical and social implications of machine learning, including bias, fairness, and transparency.
• Learn to work collaboratively on machine learning projects.
• Develop critical thinking skills to evaluate the effectiveness of machine learning algorithms.

Assessment: The assessment for this course will be based on a combination of assignments, quizzes, exams, and a final project. The final project will require students to design, implement, and evaluate a machine learning solution to a real-world problem.

Textbook: The required textbook for this course is "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurelien Geron. Additional resources and recommended readings will be provided throughout the course.

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This course provides a comprehensive understanding of the fundamental principles of mobile and wireless computing technologies. It covers the key concepts, protocols, and architectures that enable the development and deployment of mobile and wireless applications.

The course begins with an overview of mobile and wireless computing, including the history, current state, and future trends of the field. It then covers the underlying wireless technologies, such as cellular networks, WiFi, Bluetooth, and Zigbee. The course will also examine the architecture of mobile devices, including operating systems, mobile app development, and cloud computing services.

Other topics covered include mobile and wireless security, location-based services, mobile commerce, and mobile and wireless computing applications for healthcare, transportation, and education.

The course will include a combination of lectures, hands-on exercises, and assignments that will provide students with practical experience in developing mobile and wireless applications. By the end of the course, students will have a strong understanding of mobile and wireless computing and will be able to develop and deploy their own mobile and wireless applications.

Prerequisites: Students should have a basic understanding of computer networks and programming concepts. Familiarity with mobile operating systems, such as Android or iOS, is helpful but not required.

Course Goals:

• Understand the basic principles of mobile and wireless computing technologies.
• Gain practical experience in developing mobile and wireless applications.
• Understand the security, privacy, and ethical issues associated with mobile and wireless computing.
• Analyze the potential of mobile and wireless computing in various domains, such as healthcare, transportation, and education.
• Develop critical thinking skills to evaluate the effectiveness of mobile and wireless applications.
• Learn to work collaboratively on mobile and wireless computing projects.

Assessment: The assessment for this course will be based on a combination of assignments, quizzes, exams, and a final project. The final project will require students to design, implement, and deploy a mobile or wireless application.

Textbook: There is no required textbook for this course. All required readings and materials will be provided online. Additional resources and recommended readings will be provided throughout the course.

Under Construction course

This course provides an overview of artificial intelligence (AI), including its history, fundamental concepts, techniques, and applications. The course covers both the theoretical and practical aspects of AI, with a focus on developing a deep understanding of the core principles that underlie AI systems.

The course begins with an overview of the history and evolution of AI, followed by a discussion of key concepts such as knowledge representation, problem solving, reasoning, and learning. The course also covers important AI techniques such as rule-based systems, search algorithms, neural networks, and deep learning.

The course also explores important AI applications such as natural language processing, computer vision, robotics, and game playing. Additionally, students will learn about the ethical and societal implications of AI, including the impact of AI on employment, privacy, and security.

The course will include a combination of lectures, hands-on exercises, and assignments that will provide students with practical experience in developing AI systems. By the end of the course, students will have a strong understanding of the key concepts and techniques of AI and will be able to develop their own AI systems.

Prerequisites: Students should have a solid background in mathematics, computer science, and programming. Familiarity with probability theory, linear algebra, and calculus is recommended. Students should also have experience programming in a high-level language such as Python.

Course Goals:

• Understand the fundamental concepts and techniques of AI, including knowledge representation, problem solving, reasoning, and learning.
• Gain practical experience in developing AI systems using a variety of techniques, including rule-based systems, search algorithms, neural networks, and deep learning.
• Understand the ethical and societal implications of AI, including the impact of AI on employment, privacy, and security.
• Learn to work collaboratively on AI projects.
• Develop critical thinking skills to evaluate the effectiveness of AI systems.

Assessment: The assessment for this course will be based on a combination of assignments, quizzes, exams, and a final project. The final project will require students to design, implement, and evaluate an AI system to solve a real-world problem.

Textbook: The required textbook for this course is "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig. Additional resources and recommended readings will be provided throughout the course.