The B.Sc AI program is a specialized undergraduate course designed to cover advanced topics in artificial intelligence. The curriculum includes theoretical foundations, practical applications, and hands-on experience with AI technologies.
Designing and implementing machine learning models and algorithms.
Conducting research in artificial intelligence, exploring new algorithms and techniques.
Analyzing and interpreting complex datasets to extract valuable insights using AI and machine learning.
Developing systems that understand and generate human language using AI techniques.
Building applications for image and video analysis, object detection, and facial recognition.
Creating software applications that integrate AI algorithms and techniques.
Designing and developing autonomous systems and robots using AI technologies.
Providing expert advice and solutions in AI implementation and strategy across various industries.
Addressing ethical concerns and ensuring fairness and transparency in AI systems.
Utilizing AI tools and techniques to analyze data and support decision-making processes.
Using AI to analyze business data and provide insights for strategic decision-making.
Overseeing the development and deployment of AI-based products and solutions.
Using AI to detect and mitigate cybersecurity threats and vulnerabilities.
Applying AI in healthcare for tasks such as medical imaging analysis, personalized medicine, and patient care optimization.
Using AI to analyze financial data, predict market trends, and optimize investment strategies.
Overview of AI concepts, history, and its role in modern technology.
Study of machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
Fundamentals of deep neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their applications.
Techniques for processing and understanding human language, including sentiment analysis, text generation, and machine translation.
Basics of computer vision, image processing, object detection, and recognition using AI algorithms.
Discussion on ethical considerations and biases in AI systems, including fairness, transparency, and accountability.
Introduction to robotics, autonomous navigation, and control using AI techniques.
Techniques for extracting knowledge and insights from large datasets using AI and machine learning.
Study of reinforcement learning algorithms and their applications in decision-making and game playing.
Practical applications of AI in various domains such as healthcare, finance, gaming, and cybersecurity.
Hands-on experience with popular AI tools and frameworks such as TensorFlow, PyTorch, and scikit-learn.
Capstone projects that allow students to apply AI techniques to solve real-world problems and demonstrate their skills.
