Introduction to Artificial IntelligenceGrade: A
Deterministic Reasoning: Heuristic Search, Local Search, Adversarial Search, Constraint Satisfaction Problems
Probabilistic Models: Bayesian Networks, Hidden Markov Models, Kalman and Particle Filters, (Partially Observable) Markov Decision Processes
Machine Learning: Linear Models for Regression and Classification, Reinforcement Learning, Perception
INTRODUCTION TO DATA SCIENCEGrade: A
Data Analysis: Data Representation, Data Preprocessing, Data Visualization
Big Data: MapReduce, Spark, Data Streams and Big Data Algorithms
Machine Learning: Data Mining Algorithms, Clustering, Dimensionality Reduction
Principles of Information and Data ManagementGrade: A
Entity Relationship Diagram: Entity set (attributes), Relationship, n-ary relationships, Constraints: participation and cardinality, isa (class) hierarchy, Weak entities, Reification Aggregation, Manifestation.
Relational Model: Formal definition of relation (domain, arity, cardinality), Integrity constraints, Domain constraints, etc.
SQL: Basics, Subqueries, Aggregate Operators, Set operations, Cross product and conditions, Join and left outer join
Relational Algebra: Projection, Selection, Cross product, Set operators, Rename, Natural Join