Education

Hands-on introduction to learning from data: supervised/unsupervised methods, search over model spaces, probabilistic reasoning, and a taste of reinforcement learning.

Core AI toolkit: informed search, uncertainty with probabilistic models, and foundational ML concepts applied to decision-making problems.

Vectors, matrices, eigen-stuff, and decompositions that power modern graphics and machine learning pipelines.

Random variables to inference: modeling uncertainty, drawing conclusions from data, and quantifying confidence.

Algorithmic thinking at scale: graphs, greedy vs dynamic programming, reductions, and NP-completeness intuition.

Clean OO design with patterns, iterative teamwork, and modeling systems with pragmatic UML.

Ethics in practice: responsibilities of computing professionals and the broader impact of technology on society.

From CPU to packets: architecture, memory, I/O and how bits move reliably across networks.

C and assembly fundamentals, stack frames, and what actually happens beneath high-level code.

Counting cleverly: graphs, recurrences, and discrete optimization for real-world constraints.

Proofs, logic, combinatorics, and graph theory—the discrete backbone of computer science.

Designing efficient collections: lists, trees, heaps, and when each shines for performance.

Java-based tour of OOP: encapsulation, inheritance, and building maintainable software from day one.