CP2 teaches experienced programmers additional techniques to solve interview and competitive programming problems and builds on material learned in CP1. This includes specific algorithmic techniques such as [shortest paths, topological sort, MST, union find, range queries], advanced algorithms surrounding trees and DAGs, advanced problem types in [dynamic programming, backtracking/simulation, mathematics, string processing], and more. It can be viewed as a programming complement to CS 38100, with some overlap in content.

Iterative methods for solving nonlinear equations; direct and iterative methods for solving linear systems; approximations of functions, derivatives, and integrals; error analysis.

File organization and index structures; object-oriented database languages; the relational database model with introductions to SQL and DBMS; hierarchical models and network models with introductions to HDDL, HDML, and DBTG Codasyl; data mining; data warehousing; database connectivity; distributed databases; the client/server paradigm; middleware, including ODBC, JDBC, CORBA, and MOM.

The theory and practice of programming language translation, compilation, and run-time systems, organized around a significant programming project to build a compiler for a simple but nontrivial programming language. Modules, interfaces, tools. Data structures for tree languages. Lexical analysis, syntax analysis, abstract syntax. Symbol tables, semantic analysis. Translation, intermediate code, basic blocks, traces. Instruction selection, CISC and RISC machines. Liveness analysis, graph coloring register allocation. Supplemental material drawn from garbage collection, object-oriented languages, higher-order languages, dataflow analysis, optimization, polymorphism, scheduling and pipelining, memory hierarchies.

CP3 teaches experienced programmers additional techniques to solve competitive programming problems and builds on material learned in CP1 and CP2. This includes algorithmic techniques in topics such as [network flow, computational geometry, graph matching, NP-hard problems]. Primarily, CP3 prepares students to compete in programming contests, which means most class time is focused on simulating contest environments and teaching teamwork and communication alongside problem practice.

The course offers an introduction to the fundamentals principles, design strategies, and techniques needed to visually communicate, explore, and analyze data. The course focuses primarily on the visual representation of inherently non-spatial data (e.g., tables and spreadsheets, graphs and networks, trees, text, and time series), but also considers the visualization of maps and of data in geospatial context.

This course provides an integrated view of the key concepts of modern algorithmic data analytics. It focuses on teaching principles and methods needed to analyze large datasets in order to extract novel, transformative insights for the underlying application. The course emphasizes the duality between formulating questions that can be answered by statistical data analysis tools (the statistical perspective) and the algorithmic challenge of actually extracting such answers using available parallel and distributed computational resources from massive datasets. The topics cover three areas: (1) algorithmic concepts necessary for big data analytics, (2) big data systems, including data management and programming, and (3) advanced analytic methods to address characteristics of real-world big data problems.

An in-depth examination of relational database systems including theory and concepts as well as practical issues in relational databases. Modern database technologies such as object-relational and Web-based access to relational databases. Conceptual design and entity relationship modeling, relational algebra and calculus, data definition and manipulation languages using SQL, schema and view management, query processing and optimization, transaction management, security, privacy, integrity management.

Concepts for structuring data, computation, and whole programs. Object-oriented languages, functional languages, logic- and rule-based languages. Data types, type checking, exception handling, concurrent processes, synchronization, modularity, encapsulation, interfaces, separate compilation, inheritance, polymorphism, dynamic binding, subtyping, overloading, beta-reduction, unification.

Any intelligent robot system interacting with our environment needs to have perception, planning, and control methods in its cognition process. The perception module outlines the robot’s procedures to gather and interpret sensory observations into world models. The underlying planning and control modules use those world models to plan robot behaviors and their interaction with our natural environments. Therefore, this course will cover the fundamental topics in robot perception, planning, and control to design general-purpose robot cognition algorithms. Overall, this course is divided into four modules: Robot perception: This covers fundamental techniques needed for robot localization and mapping from raw 3D sensory data. Robot planning: This module will discuss robot behavior planning techniques such as A*, RRT*, and trajectory optimization. Robot Control: This introduces basic control techniques such as PID controller to execute the robot’s planned behaviors in the real world. Robot Learning: This part will briefly introduce machine learning techniques for robot decision-making and control.

Students are expected to spend at least three hours per week gaining experience with artificial intelligence systems and developing software. Basic problem-solving strategies, heuristic search, problem reduction and AND/OR graphs, knowledge representation, expert systems, generating explanations, uncertainty reasoning, game playing, planning, machine learning, computer vision, and programming systems such as Lisp or Prolog.

This course teaches important concepts and knowledge of information retrieval for managing unstructured data such as text data on Web or in emails. At the same time, students will be exposed to a large number of important applications. Students in the course will get hands on experience from homework and a course project. The first part of the course focuses on general concepts/techniques such as stemming, indexing, vector space model, and feedback procedure. The second part of the course shows how to apply the set of techniques on different applications such as Web search, text categorization, and information recommendation.

The goal of this course is to teach students how to design useful and usable interactive systems that address important needs of people. Students will experience the entire user-centered design life cycle, from need finding to usability evaluation. Topics covered in the course include user-centered design principles, usability heuristics, need-finding methods such as semi-structured interviews and contextual inquiry, quick prototyping techniques, usability evaluation methods such as hallway testing and human-subjects user study, and theories about user interaction and decision making. As we are entering a new era of AI, the course will also include a brief introduction on how to apply the HCI principles and techniques to AI-powered systems. This course is project-based. Students will form project teams among themselves to work on a semester-long project and apply the user-centered design principles, theories, and techniques that they have learned in class to build a useful and usable interactive system such as a mobile application. This course is also highly interactive, including a series of design studios and in-class activities that require active participation, communication, and discussion with other students.

Turing machines and the Church-Turing thesis; decidability; halting problem; reducibility; undecidable problems; decidability of logical theories; Kolmogorov complexity; time classes; P, NP, NP-complete; space classes; Savitch’s theorem, PSPACE-completeness, NL-completeness; hierarchy theorems; approximation theorems; probabilistic algorithms; applications of complexity to parallel computation and cryptography. Typically offered Fall Spring.

This course will cover the key concepts and methods used in modern Natural Language Processing (NLP). Throughout the course several core NLP tasks, such as sentiment analysis, information extraction, syntactic and semantic analysis, will be discussed. The course will emphasize machine-learning and data-driven algorithms and techniques, and will compare several different approaches to these problems in terms of their performance, supervision effort and computational complexity.

This introductory course will cover many concepts, models, and algorithms in machine learning. Topics include classical supervised learning (e.g., regression and classification), unsupervised learning (e.g., principle component analysis and K-means), and recent development in the machine learning field such as variational Bayes, expectation propagation, and Gaussian processes. While this course will give students the basic ideas and intuition behind modern machine learning methods, the underlying theme in the course is probabilistic inference.