Course Description:

• Search: Implement and analyze various search algorithms to navigate problem spaces efficiently, optimizing for factors like path cost, time complexity, and memory usage. Explore informed and uninformed search techniques, including heuristic search and local search.
• Logic: Formalize knowledge and reasoning using logical representations, including propositional logic and first-order logic. Apply inference rules and logical deduction to derive new knowledge and solve logical problems.
• Planning: Design and implement planning algorithms to generate sequences of actions that achieve desired goals in complex environments. Explore different planning paradigms, such as classical planning, planning with uncertainty, and hierarchical planning.
• Learning: Master fundamental machine learning algorithms and techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning. Build, train, and evaluate models for tasks such as classification, regression, and clustering.
• Speech and Language Processing: Develop an understanding of computational approaches to natural language understanding and generation. Explore techniques for tasks like text processing, sentiment analysis, machine translation, and speech recognition.
• Vision and Image Processing: Learn about algorithms and techniques for processing and interpreting visual information. Cover topics such as image classification, object detection, image segmentation, and 3D vision.