Artificial Intelligence

• Apply logical reasoning and programming to produce solutions for real-world problems.
• Use probabilistic inference to navigate uncertain information efficiently.
• Determine appropriate machine learning methods for a given scenario or dataset.
• Evaluate the challenges in perception systems for AI.
• Utilize sensors to execute perception tasks and their applications in intelligent systems.
• Apply algorithms to train an image classifier.
• Design an agent that can plan and act to achieve given objectives using noisy sensors and actuators.

Statistical Machine Learning

• Distinguish between supervised learning and unsupervised learning.
• Apply common probability distributions in machine learning applications.
• Use cross validation to select parameters.
• Use maximum likelihood estimate (MLE) for parameter estimation.
• Implement fundamental learning algorithms such as logistic regression and k-means clustering.
• Implement more advanced learning algorithms such as support vector machines and convolutional neural networks.
• Design a deep network using an exemplar application to solve a specific problem.
• Apply key techniques employed in building deep learning architectures.