Computer Science Courses

CSCI 3311

Visualization (fall)

Prerequisites; CSCI 1102

Data can capture a snapshot of the world and allow us to understand ourselves and our communities better. With ever-increasing amounts of data, the ability to understand and communicate data is becoming essential for everyone. Visualization leverages our visual perception to provide a powerful yet accessible way to make sense of large and complex data. It has been widely adopted across disciplines, from science and engineering to business and journalism, to combat the overabundance of information in our society. In this course, students will learn to acquire foundational knowledge about how to design effective visualizations for analysis and presentation based on theories and principles from graphic design, perceptual psychology, and cognitive science. Students will also learn practical skills about how to rapidly explore and communicate data using Tableau and build interactive visualization products (e.g., articles, tools, and systems) using web-based frameworks including D3.js and Vega-Lite.

 

CSCI 3333

Computer Graphics (periodically)

Prerequisite: CSCI 1102

This course introduces algorithms and techniques involved in representing, animating, and interacting with three-dimensional objects on a computer screen. The course will involve significant programming in Java and OpenGL.

 

CSCI 3335

Principles of Multimedia Systems (periodically)

This course introduces principles and current technologies of multimedia systems. Topics include multimedia systems design, multimedia hardware and software, issues in effectively representing, processing, and transmitting multimedia data including text, graphics, sound and music, image, and video. Image, video, and audio standards such as JPEG, MPEG, H.26x, Dolby Digital, and AAC will be reviewed. Applications such as video conferencing, video streaming, multimedia data indexing, and retrieval will also be introduced.

 

CSCI 3341

Artificial Intelligence (fall, alternate years)

Prerequisites: CSCI 1102, CSCI 2244

This course addresses the modeling and design of intelligent computational software. Artificial intelligence ideas have played a key role in the development of master-level board game players, natural language understanding, self-driving vehicles, and the predictive modeling methods used in data mining. Course topics include perception and action, search techniques such as A* heuristic search and adversarial search, knowledge representation formalisms including logic and probability, and an introduction to machine learning. Programming assignments will be given throughout the course.

 

CSCI 3343

Computer Vision (fall, alternate years)

Prerequisites: CSCI 1102, CSCI 2244

Computers are gaining abilities to “see” things just like our vision system. Face recognition has been embedded in almost all the digital cameras. Car detection and tracking have been used in self-driving vehicles. Modern search engines are not only able to find similar text patterns but also able to search for similar objects in huge image databases. This course introduces principles and computational methods of obtaining information from images and videos. Topics include image processing, shape analysis, image matching, segmentation, 3D projective geometry, object tracking, human pose and action, image retrieval, and object recognition.

 

CSCI 3344

Mobile Application Development (spring)

Prerequisite: CSCI 1102

This is a project-oriented course focusing on the development of applications for smart phones and tablets. The course is currently taught using Google’s Android platform. The course will focus on software and user interface design, emphasizing best practices. The course examines issues arising from the unique characteristics of mobile input devices including touch and gesture input, access to a microphone, camera, and orientation and location awareness. We will also explore engineering aspects of targeting small memory platforms and small screens. Students will be required to design and develop substantial projects by the end of the course.

 

CSCI 3345

Machine Learning (spring, alternate years)

Prerequisite: CSCI 1102, CSCI 2244

This course provides an introduction to computational mechanisms that improve their performance based on experience. Machine learning can be used in engineered systems for a wide variety of tasks in personalized information filtering, health care, security, games, computer vision, and human-computer interaction, and can provide computational models of information processing in biological and other complex systems. Supervised and unsupervised learning will be discussed, including sample applications, as well as specific learning paradigms such as decision trees, instance-based learning, neural networks and deep learning, Bayesian approaches, meta-learning, and clustering. General concepts to be described include feature space representations, inductive bias, overfitting, and fundamental tradeoffs.

 

CSCI 3346

Data Mining (spring, alternate years)

Prerequisite: CSCI 1102, CSCI 2244

The goal of data mining is to discover patterns in data that are informative and useful. This course provides an overview of the field of knowledge discovery and data mining, which deals with the semi-automated analysis of large collections of data that arise in contexts ranging from medical informatics and bioinformatics to e-commerce and security. The course will cover fundamental data mining tasks, relevant concepts and techniques from machine learning and statistics and data mining applications to real-world domains such as e-mail filtering, gene expression, analysis of biomedical signals, and fraud detection.

 

CSCI 3347

Robotics (spring, alternate years)

Prerequisite: CSCI 1101

This is a hands-on laboratory course about the programming of robots. Topics covered include locomotion, steering, moving an “arm” and “hand,” dealing with sensory input, voice synthesis, and planning. Students will complete several projects using the robots in the Boston College Robotics Laboratory.

 

CSCI 3349

Natural Language Processing (fall)

Prerequisites; CSCI 1102 and CSCI 2244

In this hands-on course, we study natural language processing (NLP), the subfield of artificial intelligence focused on analyzing, producing, and understanding human language. Using models and algorithms from formal language theory, statistics, and machine learning, we will explore methods for gaining insight into the structure and meaning of text. We will apply these methods to tasks such as information extraction, sentiment analysis, and machine translation. Students will work in teams to collect data and to implement their own NLP applications.

 

CSCI 3353

Object Oriented Design (fall)

Prerequisite: CSCI 1102

CSCI 1102 introduced you to the basic concepts of object-oriented programming: classes, inheritance, and polymorphism. In this course, we look at object-oriented programming from a higher level, and focus on the design of object-oriented software. As an analogy, consider a list—it is a lot easier to understand its operations by drawing pictures than by looking at code. Similarly, you will learn how to draw pictures to describe the design of an object-oriented program. And from these pictures we can develop design rules, such as "separate the model from the view" and "program to interfaces". We will also go over fundamental design patterns that give us a simple way to talk about complex interactions of classes.

Another analogy is the difference between an architect and a building contractor. An architect designs the building, and is responsible for its usability, aesthetics, and feasibility. The contractor follows the plan, making low-level decisions about each component. Both are professionals, but the architect gets to be more creative and is often more highly valued. This course teaches you how to be a software architect.

Homework assignments will involve the design of inter-related classes and their implementation in Java.

 

CSCI 3356

Software Engineering (spring, alternate years)

Prerequisite: CSCI 3353

This course covers industrial system development using object-oriented techniques. Students will learn a methodical approach to the development of software and will use this methodology to design, implement, test and evolve Java applications. Students will work in teams to develop applications, experiencing the different roles that are required on projects in industry.

 

CSCI 3357

Database System Implementation (spring, alternate years)

Prerequisite: CSCI 1102

This course will not cover the use of commercial database systems; students interested in that topic should consider taking CSCI 2257.

A database system is an amazingly sophisticated piece of software. It contains (1) a language interpreter, for processing user queries; (2) query rewrite strategies, for transforming inefficient queries into more efficient ones; (3) complex algorithms for indexing data, to support fast access; (4) a separate file system from that of the operating system, for managing the disk efficiently; (5) recovery mechanisms, for ensuring database integrity when the system crashes; and (6) an ability to handle concurrent accesses from multiple users. In this course we examine the various algorithms, data structures, and techniques for implementing these features. And to make these theoretical ideas concrete, we will also examine the Java source code for a real-life database system – first to see how it works, and then to write our own additions and improvements to it.

The goals of this course go beyond the study of database systems principles. The algorithms you learn can be used in many other systems and applications. And you get to see how a large software system is structured. The course requires extensive Java programming. You do not need experience using a commercial database system; you will learn all necessary database concepts during the course.

 

CSCI 3359

Distributed Systems (fall, alternate years)

Prerequisite: CSCI 2271

In this course you will learn the major paradigms of distributed computing, including client-server and peer-to-peer models. We will study how each model addresses the problems of communication, synchronization, performance, fault-tolerance, and security. You will learn how to analyze the correctness of distributed protocols and will be required to build distributed applications.

 

CSCI 3362

Operating Systems (fall, alternate years)

Prerequisite: CSCI 2271

This course will provide a broad introduction to software systems with emphasis on operating system design and implementation. Its objective is to introduce students to operating systems with main focus on resource management and interfacing issues with hardware layers. Particular emphasis will be given to process management (processes, threads, CPU scheduling, synchronization, and deadlock), (virtual) memory management (segmentation, paging, swapping, caching) with focus on the interplay between architectural components and software layers. If there is time, we will investigate and discuss these same issues for distributed systems. The course programming assignments will be in Java/C.

 

CSCI 3363

Computer Networks (spring, alternate years)

Prerequisite: CSCI 2271

This course studies computer networks and the services built on top of them. Topics include packet-switch and multi-access networks, routing and flow control, congestion control and quality-of-service, resource sharing, Internet protocols (IP, TCP, BGP), the client-server model and RPC, elements of distributed systems (naming, security, caching, consistency) and the design of network services (peer-to-peer networks, file and web servers, content distribution networks). Coursework involves a significant amount of Java/C programming.

 

CSCI 3366

Principles of Programming Languages (spring, alternate years)

Prerequisite: CSCI 1102, CSCI 2243

Starting with a simple language of expressions, this course develops a sequence of progressively more expressive programming languages keeping in mind the conflicting constraints between the expressiveness of the language and the requirement that it be reliably and efficiently implemented. The course focuses on these essential concepts and the run-time behavior of programs. Type systems play an essential role. By understanding the concepts the student will be able to evaluate the advantages and disadvantages of a language for a given application.

 

CSCI 3367

Compilers (periodically)

Prerequisite: CSCI 2271

Compilers are programs that implement high level programming languages by translating programs in such languages into machine code or some other easy to process representation. This course deals with the principles and techniques used in the design of compilers. Topics include parsing, static analysis, translation, memory management, and code optimization. This course includes a significant programming project.

 

CSCI 3372

Computer Architecture and Lab (spring, alternate years, 4 credits)

Prerequisites: CSCI 2272

This course discusses hardware considerations in computer design. Topics include hardware description languages, arithmetic and logic units, input/output circuits, memory hierarchy, instruction programming and control, data paths, pipelining, processor design, and advanced architecture topics. CSCI 3372 includes laboratory-based computer hardware activities in which students design and build digital circuits related to the topics of the course.

 

CSCI 3381

Cryptography (fall, alternate years)

Prerequisites: CSCI 2243 or MATH 2216 or permission of instructor.

When you log onto a secure web site, for example to pay a bill from your bank account, you need to be assured of certain things: Is your communication private? An eavesdropper should not be able to determine what information you and the bank are exchanging. Does the website you are communicating with really belong to the bank? A third party should not be able to successfully impersonate the bank. Are you you? A third party should not be able to impersonate you and make payments from your account. Are the messages you and the bank receive from each other the same ones that were sent? No one should be able to alter the messages in transit without this being detected.

Behind the scenes, an extraordinary series of computations takes place to ensure that these security requirements are met. This course some sophisticated ideas from both mathematics and computer science that make it all work. We will begin the course with a look at some classical cryptographic systems that were in use before the advent of computers, then study modern block ciphers, both the general principles behind their construction and use, and some details about widely-used systems: the Data Encryption Standard (DES) and Advanced Encryption Standard (AES). These are symmetric systems in which the parties share some secret information (a key) used for both encryption and decryption. Cryptography was profoundly changed by the invention, in the late 1970's, of asymmetric, or public-key cryptosystems, in which the two parties do not need to share a secret in order to communicate securely. We will study public-key cryptosystems like RSA, cryptographic hash functions, schemes for digital signatures, and zero-knowledge identification schemes. We'll finish the course looking at some real-world cryptographic protocols (for example, SSL), more speculative protocols (electronic elections or digital cash), and some different ideas for the construction of cryptosystems (quantum cryptography).

 

CSCI 3383

Algorithms (fall)

Prerequisites: CSCI 1102, CSCI 2243, CSCI 2244

Algorithms are the basis of computing, and their study is, in many ways, the essence of computer science. In this course we study several algorithm-creation techniques, such as "divide and conquer", "dynamic programming", and "be greedy". We shall also learn mathematical tools to help us analyze the efficiency of our algorithms. These techniques are illustrated by the study of interesting algorithms of practical importance.

 

CSCI 3390

Topics in Computer Science (periodically)

This course can be taken multiple times for credit. It covers new and other interesting topics not included among the department's regular course offerings. Two sections will be offered in spring 2018, as described below.

CSCI 3390-01
Everyone should know how to design parallel algorithms. Even a laptop or cellphone has multiple CPU cores at our disposal these days. In this hands-on, project-oriented course you will learn the main ideas of parallel computing with GPUs. Our focus will be on the CUDA programming language. You will learn about GPU architectures, parallel algorithms, CUDA libraries and GPU computing applications. Prerequisites: CSCI 3383 / 2271 / 2244, and MATH 2210 / 2202, or permission of the instructor.

CSCI 3390-02
We will study natural language processing, the subfield of artificial intelligence focused on analyzing, producing, and understanding human language. Using models and algorithms from formal language theory, statistics, and machine learning, we will explore methods for gaining insight into the structure and meaning of text. We will apply these methods to tasks such as information extraction, sentiment analysis, and machine translation. Prerequisite: CSCI 1102.