Term(s) offered: Fall

Algorithms are at the heart of any computer-related task. In this course, we will teach how to approach the meta-task of algorithm building, as well as look at individual algorithms.  We will use mathematical tools for designing and analyzing our algorithms, and get some simple hands-on coding experience. If you’re a non-CS major, or someone who cares more about applications than theory, then your chances are this course is the one you want.

At the completion of this course, you will be able to:

• Know, use, and, if necessary, modify a range of algorithms and data structures for well-known problems.
• Given a problem, be able to develop an algorithmic solution to it.
• Be able to analyze the above solution for correctness and efficiency.
• Given different algorithms, be able to analyze and compare them.
• Have basic knowledge of complexity, upper and lower bounds.
• Have basic experience in coding simple algorithms.

Term(s) offered: Fall

Prerequisite(s): Experience with programming, data structures, and algorithms will be assumed. Assignments will involve substantial amount of programming in Python. In addition, we will encounter math of various kinds, including linear algebra, probability theory, and basic calculus.

This course covers the fundamentals of Artificial Intelligence, and is aimed at M.S., early Ph.D., and advanced undergraduate students in Computer Science and Data Science, as well as students in other related fields who have a strong computing background.  Topics will include (tentatively):

• AI overview: Goals, history, progress, challenges.
• Problem solving and search: Uninformed search, heuristics, A*, local search.
• Applied search: Game playing, constraint satisfaction, planning.
• Reasoning under uncertainty: Uncertainty representation, probabilistic models, probabilistic inference, Bayesian and Markov networks.
• Machine learning: Decision trees, neural networks, support vector machines.
• Applications: Computer vision, natural language processing, robotics.

Term(s) offered: Spring

Prerequisite(s): It is assumed that you can program in various styles (imperative, functional, and object-oriented), have knowledge of algebra and elements of discrete math, as well as data structures and algorithms.

Introduction to database concepts and systems. Topics include:

• Database models and systems: especially relational, object-oriented, semi-structured, and graph data models
• Query languages and aspects of database programming
• Database design and modeling
• Components of query processing
• Data structures and algorithms for efficient query processing
• Introduction to transaction management: concurrency and recovery

Term(s) offered: Spring

Prerequisite(s): CSCI B551 or equivalent is required. You will need to be proficient in a general-purpose programming language (Python or C/C++); you should be able to implement basic matrix operations using basic data structures of the language (e.g. matrix multiplication using arrays). Exposure to linear algebra, basic calculus, machine learning, graph theory, probability theory, geometry, and statistics will be extremely helpful.

This is an introductory course in computer vision.  We will give a broad overview of the field, with a slight bias towards some topics to reflect current research trends (e.g. object recognition, deep learning). The emphasis will be on algorithms, mathematical models, and techniques that are broadly applicable to many problems not only in vision but also in other areas of AI and CS. Topics will include

(tentatively and not necessarily in this order):

• Basic image manipulation: digital image representation, image filtering, morphology
• Feature detection: edge detection, corner detection, invariant interest point detection Segmentation
• Recognition: subspace methods, hausdorff/chamfer matching, bags-of-words, pictorial structures
• Learning for vision: support vector machines, neural networks, deep learning
• Geometry and image transformations: camera models, image transformations, 2d & 3d geometry
• Graphical models: Markov models, Markov Random Fields, belief propagation, graph cuts
• Motion: Optical flow, parametric motion
• Reconstruction: stereo, structure from motion, shape from X, image restoration

Term(s) offered: Fall, Spring, Summer

This course is designed to help students experience the complexities and nuances of applying data science in the real world. Students will work in teams to tackle real-world problems in ongoing and new projects defined by a project sponsor. Project sponsors can be academics or industry practitioners. Students will need to work with the project sponsor and other team members to understand the problem domain, decide on a role, identify where their data science skills can be applied, and to work on a solution; in this regard, much of the course is about moving from ambiguity to an achievable outcome. During the course, students will also study aspects of data science consulting and project management through weekly reading assignments. The emphasis in this course is on the learning experience over the technical outcomes in the projects. The course is designed for second year MS data science students.

After completing this course, students will have practical experience working in a complex team environment using their skills to solve a real data science problem in an application domain; be able to go into an ambiguous situation and identify concrete opportunities; and understand multiple perspectives on data science consulting and project management.

Term(s) offered: Fall, Spring, Summer

Graduate Internship credit can be awarded to students undertaking a significant experiential learning opportunity through a company, organization, nonprofit, etc. Students are responsible for securing their own internships, but should contact SICE Career for assistance and resources. Students will participate in an internship for at least 6 weeks, with no less than 160 hours of supervised work. A student not earn more than three (3) credit hours in the course and the experience must be integral to his/her curricula.

Term(s) offered: Fall, Spring, Summer

Independent study courses allow students to conduct individualized projects under the supervision of a faculty member. Up to three (3) credit hours may be earned to conduct research or to explore specific areas of data science that are not well covered by any specific formal course. The course is managed by a supervising faculty in conjunction with the proposed learning goals of the student. The student and the faculty discuss and propose goals, topics and projects.

Term(s) offered: Spring

The course discusses advanced signal processing topics as an application of machine learning. Hands-on signal processing tasks are introduced and tackled using a problem-solving manner, so students can grasp important machine learning concepts. The course can help students learn to build an intelligent signal processing system in a systematical way.

Term(s) offered: Fall, Spring

This course covers basic concepts on programming models and tools of cloud computing to support data intensive science applications. Students will get to know the latest research topics of cloud platforms, parallel algorithms, storage and high level language for proficiency with a complex ecosystem of tools that span many disciplines. The course has the following objectives:

• Provide a basic introduction to cloud computing
• Introduce the concept of cloud data centers
• Get familiar with cloud infrastructure as a Service such as OpenStack, Azure, or AWS
• Get familiar with cloud infrastructure such as Docker and Kubernetes
• Program cloud services
• Understand the differences between virtual machines and containers
• Develop sophisticated programming language independent REST services
• Learn advanced programming models for clouds such as Map/Reduce, Messaging, and GraphQL
• Exploration of Go for cloud computing
• Demonstrate knowledge of clouds while developing a significant project
• Explore state-of-the-art cloud technologies and services while providing a section and summary and commenting on its use for the cloud
• Learn how edge computing is enhancing cloud services and infrastructure
• Learn how to set up a cloud based on using commodity hardware

Term(s) offered: Fall

This one-semester college course, “Introduction to High Performance Computing”, is offered as an entry-level hands-on learning experience in supercomputing providing the essential concepts, knowledge, and skills needed to begin a career either in supercomputing or as an effective means of achieving within the scope of other disciplines dependent on it. This course will also serve those interested in HPC engineering and design, software development, or system administration. The goal of the course is to engender a new generation of computer and computational scientists expert in the development, operation, and application of high performance computing systems prepared to address this future challenges demanding capability and expertise in HPC. The course is interdisciplinary combining critical elements from hardware technology and architecture, system software and tools, and programming models and application algorithms with the cross-cutting theme of performance management and measurement. It provides hands-on experience with strong educational reinforcement through experimental exercises.The topic areas to be covered by this one-semester course include:

• Introduction and overview of HPC,
• Large scale applications and parallel algorithmic methods,
• Enabling technologies for logic, memory, and communication,
• Parallel architectures including SMPs, commodity clusters, MPPs, and GPUs
• Performance metrics, monitoring, measurement, and benchmarking,
• Programming methods and tools including MPI, OpenMP, and OpenACC, and
• Scientific visualization, performance and correctness debugging, and resource management.

Term(s) offered: Fall

This course teaches the pipeline for building state-of-the-art deep learning-based intelligent systems. It covers general training mechanisms and acceleration options that use GPU computing libraries and parallelization techniques running on high performance computing systems. The course also aims at deploying the networks to the low-powered hardware systems.

Term(s) offered: Fall

This class investigates the use of clouds running data analytics collaboratively for processing Big Data to solve problems in Big Data Applications and Analytics. Case studies such as Netflix recommender systems, Genomic data, Sports, Health, and more will be discussed.

The course has the following objectives:

• Provide an introduction to Big Data
• Provide an introduction to Big Data Analytics
• Provide overviews of different Big Data Application areas
• Explore state-of-the-art big data and cloud technologies and services while providing a write up about it and exploring it practically with a section that you develop
• Enforce the theoretical knowledge with a project that you conduct in one of the application areas.

Term(s) offered: Spring

The visual representation of information requires a deep understanding of human perceptual and cognitive capabilities, data mining and visualization algorithms, interface and interaction design, as well as creativity. Data—such as twitter, books or social networks—is typically non-spatial and needs to be mapped into a physical space that represents relationships contained in the information faithfully and efficiently. If done successfully, data visualizations combine human and machine intelligence to solve tasks that neither could accomplish alone.

This course provides an overview about the state-of-the-art in information visualization. It teaches the process of producing effective temporal, geospatial, topical, and network visualizations. Students get the chance to use tools such as Tableau, D3.js, OpenRefine, Gephi, and Plot.ly. Students have the opportunity to collaborate on real-world projects for a variety of clients.

Specifically, the course covers:

• visualization frameworks that guide development
• data analysis algorithms that enable extraction of structures and trends in data
• major visualization and interaction techniques
• discussions of systems that drive research and development
• trends, opportunities, and challenges in the field

Term(s) offered: Spring

In this class, you will focus on advanced concepts that build upon ENGR E516. This includes DevOps, Julia, and any topic of E516 with enhancements. The focus will allow you to deliver contributions based on your knowledge as part of sections, a chapter you propose to add to the lecture notes, and a more intensive project.

When students complete this course, they should be able to:

• Have an advanced understanding of issues involved in designing and applying modern cloud technologies using the latest developments.
• Gain hands-on laboratory experience.
• Understand the Apache Big Data Software Stack.
• Apply knowledge of mathematics, science, and engineering.
• Understand research challenges and important application areas of clouds
• Have advanced skills in teamwork with peers.
• Be able to use DevOps technologies.

Term(s) offered: Fall

This course is an extensive survey of network security. The course materials cover threats to information confidentiality, integrity, and availability in different Internet layers, and defense mechanisms that control these threats. The course also provides a necessary foundation on network security, such as cryptographic, primitives/protocols, authentication, authorization and access control technologies; and hands-on experiences through programming assignments and course projects.

Term(s) offered: Fall

The course will use the tools of economics to better understand computer security. This is not a course in economics research in that no new tools will be discovered and no new ground will be broken in economic theory. The understanding of economics required for this course is modest, and a strong mathematical background with no economics will certainly suffice. There is no textbook. The course will be based on a series of research papers, primarily drawn for the series of Workshops on Economics of Information Security.

At its core, this course should improve your decision-making for any organizations requires for its security professionals. In addition to the fundamental language of decision-making, the course will identify the dimensions of organizational and economic behavior that impinge upon the success of organizational technical choices.

Term(s) offered: Fall, Spring

If you want to become a machine learning practitioner, a better problem solver, or maybe even consider a career in machine learning research, then this course is for you. However, for a novice, the theoretical concepts behind machine learning can be quite overwhelming.

This course focuses on introducing theoretical concepts and algorithms in a step-by-step manner, while infusing them with intuition, examples and python jupyter notebooks. In this spirit, you will study core ML algorithms, while also working through numerous example applications of machine learning. Concrete examples help illustrate the broader concepts by putting the learned material directly into action. This combination of theory and hand-ons will help you master core ML concepts and algorithms that are used, not only in Silicon Valley but, throughout the world, while also offering intuitive yet informative explanations of how machine learning algorithms work, how to use them, and most importantly, how to avoid the most common pitfalls.

For those with a stronger interest in ML theory and development this course will provide an optional track that will focus on delving into the theory a little more deeply, and that culminates in coding up core ML algorithms from scratch and possible extending them.

Term(s) offered: Spring

This course is targeted at graduate students. This course covers the design and analysis of secure systems, including identifying security goals and risks, threat modeling, defense, integrating different technologies to achieve security goals, developing security protocols and policies, implementing security protocols and secure coding. Some real-world scenarios that have many security requirements will be studied.

Term(s) offered: Fall

Data is abundant, and its abundance offers potential for new discovery as well as economic and social gain. However, data can be difficult to use, not to mention noisy and inadequately contextualized. There can be too big a gap from data to knowledge due to limits in technology or policy not easily combined with other data. This course will examine the underlying principles and technologies needed to capture, clean, contextualize, store, access, and trust data for a repurposed use. Students in this course will be introduced to capabilities and benefits of big data, key components of big data projects, and major steps in data analysis and visualization.

The following concepts are covered in the course:

• Big data in science and business
• Data pipelines
• Complexity in software systems
• Modeling data storage in noSQL stores
• Data replication
• Distributed computing
• Data coding and cleaning
• Data provenance
• Data trustworthiness
• Economies of data sharing

It is expected that a student will put in 6-7 hours a week every week into the course which includes time spent in readings, reflections, and engaging with instructional content.

Term(s) offered: Fall, Spring, Summer

Advanced Data Science Onramp contains mini courses with the goal to build and enhance your data science skills, which are oftentimes demanded or desired in data science related jobs. Each mini course will be counted as one credit hour. You may enroll in 1-3 credit hours, which means that you can select 1 or 2 or 3 mini courses. Please note that no more than three (3) credit hours can be earned in On-Ramp courses (Basic or Advanced) effective Spring 2019.

A list of Advanced On-Ramps include:

• Advanced AWS
• Basics of Scala
• Deep Learning Principles
• Introduction to Hadoop Framework
• Introduction to Spark
• Kaggle Cases
• Machine Learning with Spark

Term(s) offered: Fall

The goal of this course is to develop practical skills needed to perform applied data science research. The course is organized around each stage of the data science workflow (setting expectations, exploratory data analysis, modeling, interpreting, and communicating results) and covers algorithms, best practices, and evaluation criteria. Both good and bad applications examples will be discussed to help the student develop a deeper understanding and intuition about the choice of algorithm or visualization for the data task, the development of the best practices, and the methods for evaluating results of different approaches.Lectures and reading will provide students with theoretical foundation for research and hands-on practice and assignments will help develop practical skills.

Term(s) offered: Fall, Spring, Summer

Basic Data Science Onramp contains mini courses with the goal to build and enhance your data science skills, which are oftentimes demanded or desired in data science related jobs. Each mini course will be counted as one credit hour. You may enroll in 1-3 credit hours, which means that you can select 1 or 2 or 3 mini courses. Please note that no more than three (3) credit hours can be earned in On-Ramp courses (Basic or Advanced) effective Spring 2019.

A list of Basic On-Ramps include:

• Data Processing
• Feature Selection
• Machine Learning Principles
• Machine Learning with Java
• Machine Learning with Python
• Machine Learning with R
• Model Fitting and Evaluation
• Natural Language Processing in Python
• Tableau
• Web Scraping

Term(s) offered: Fall, Summer

From TV news to cutting-edge scientific papers, from a home office to the largest companies in the world, data visualization is extensively used to reveal patterns in data and to tell stories. More and more data is collected, and more and more decisions are made through data analysis. Data visualization is indispensable for understanding data, and thus is an essential skill for every knowledge worker. This course is an introduction to basic statistical data analysis and visualization. We will learn fundamentals of data visualization in the context of perception, integrity, design, statistics, types of 1 2 data, and visualization techniques. The hands-on exercises using the Python stack aim to equip you with practical data visualization skills and they will be an integral part of the course.

By the end of the course, you are expected to be able to understand, explain, and manipulate basic types of data, analyze them by applying basic exploratory visualization techniques, and create explanatory visualizations. You will also be able to evaluate the effectiveness of data visualizations based on the principles of human perception, design, types of data, and visualization techniques.

Term(s) offered: Spring (Special 2^nd 8-weeks course through Kelley School of Business)

This course is designed to provide a foundation in the use of modeling techniques in managerial decision-making. The course will cover three separate areas of modeling – forecasting, computer simulation and optimization. Computer simulation will be introduced and we will follow up on more advanced aspects of the topics in this course.

In particular, we will concentrate on input and output analysis for simulation models.  In optimization, we will cover several different areas including linear programming, integer programming, nonlinear programming and genetic algorithms. We will also spend two weeks on forecasting and cover a broad overview of key forecasting techniques.

Upon completion of this course, students should be able to:

• Understand how analytical techniques and tools are used to provide solutions to operational problems in various business functional areas including finance, economics, operations, and marketing.
• Develop analytical models to analyze various business problems.
• Recommend sound solutions to complex business problems based on the results of their analysis.
• Solve complex problems using analytical techniques and tools on spreadsheets using various spreadsheet based add-ins. These include using Excel Solver for linear and integer programming problems, StatTools for statistical analysis, and @RISK for probabilistic simulations and risk analysis.

Term(s) offered: Fall

Prerequisite(s): Python and R

Natural Language Processing (NLP) has become an essential skill in many daily tasks for Data Scientists. From cleaning and parsing to extracting and computing, a scientist often faces challenging questions:

1. Data Wrangling - how to process and clean structured and unstructured data
2. Data Collection - how to extract text information
3. Data Analysis - how to summarize and categorize text data
4. Data Understanding - how to interpret natural language data

In this course, you will be introduced to NLP basics and will be guided though the most common NLP tasks for data analysis. In the first half of the course you will learn NLP processing skills. In the second half of the course you will dive into the domain-specific NLP techniques for data analysis featuring Healthcare, Banking, Marketing, Customer Service, and Technology domains.

This course is designed to prepare you for more advanced Data Science courses (Machine Learning and Deep Learning) as well as for more linguistic theory-oriented courses (Computational Linguistics) to enhance and refine your NLP skills.

Term(s) offered: Fall, Spring, Summer

This course provides a gentle, yet intense, introduction to programming in Python for students with little or no prior experience in programming. Python is an open-source language that allows rapid application development of scalable software systems is object-oriented by design and provides an excellent platform for doing data science. The course will focus on planning and organizing programs, and developing high quality, working software that solves real world problems.

Students will:

• Learn how to design and implement scalable Python programs that solve real world problems, with focus on Data Science applications.
• Learn top-down and object oriented approaches to software design.
• Learn data structures and algorithms used in numeric and text data processing.

Term(s) offered: Spring, Summer

Database is the central focus in data science to store and manage data. Relational databases have empowered the main industries for decades and are still widely adopted. In the new era of big data, the database landscape is undergoing significant change. Many non-relational databases become an important part of the enterprise data architecture of companies. Relational databases were developed long before the Internet and the Web to tackle the issues of central-controlled data storage and management. NoSQL databases emerged with the rise of Internet and Web applications to connect companies with customers (i.e., online or mobile) and to develop with agility to adapt to faster changes. The new challenges of being agile and being able to accommodate data variability/data integration drive enterprises to turn to NoSQL database technology. It is important for every data scientist to master the skills of the current database and know about the future of databases in a world of NoSQL.

This course aims to provide the basic overview of the current database landscape, starting with relational databases, SQL, and moving to several different NoSQL databases, such as XML database, MongoDB, Neo4j, Redis, Cassandra, and HBase.

Term(s) offered: Spring

The course is a hands-on course providing a guided platform to learn and practice critical time-series analysis skills. This course will cover time series regression and exploratory data analysis, ARMA/ARIMA models, model identification/estimation/linear operators, Fourier analysis, spectral estimation, and state space models. The analyses will be performed using the freely available package astsa, xts, zoo. Lectures and reading are obligatory. R [Rstudio & Rmarkdown] and GitHub [Github Desktop] are required.

Term(s) offered: Spring

Networks, or graphs, provide a unifying framework to study complex systems, such as living organisms, societies, and many techno-social systems. This graduate-level course focuses on the fundamental concepts as well as key applications of network science. The course will cover recent advancement of network science, with respect to statistical properties and models of real-world networks, network algorithms, and practical applications. Topics include: how information and diseases spread in our society, measures and algorithms for quantifying importance, link prediction, and community detection.

By the end of the course, students are expected to be able to identify, construct, and analyze networks by choosing and applying appropriate methods and algorithms. Students are also expected to be able to explain, both mathematically and conceptually, the key network concepts and statistical properties, and their implications.

Term(s) offered: Spring

With the exponential growth of the Web in the past decades, we are facing a flood of information.

The success of GYM (Google, Yahoo and MSN) has shown that Information Retrieval is a key component to assist users to access target information based on their need. The course introduces information retrieval theories and concepts underlying all search applications. We will investigate techniques used in modern search engines and demonstrate their significance by experiment.

At the end of this course, students will be able to

• Understand the mechanism of the most important and up-to-date retrieval theories and models
• Be able to design and implement search engines using retrieval models
• Work in teams or individual to build your own search components and interfaces
• Learn ow information retrieval is used in other related fields, i.e. digital library, online shopping, multimedia environment
• Enhance your search skills via various of search engines such as Google and Bing

Term(s) offered: Fall

This course is intended to introduce you to the burgeoning field of Social Media Mining. We will explore what, exactly, is meant by the term "Social Media," and why anyone would be interested in mining it. After establishing some basic definitions and motivations, we will spend the rest of the course learning various techniques and methods that are currently employed to extract meaningful signals from the growing flood of social media data. In pursuit of this goal, I will provide hands-on, guided exercises using Python, and we will also read academic papers where authors share their methods, research questions, and insights mining the social web.

Term(s) offered: Fall, Spring, Summer

This course introduces the basic concepts of statistical inference through a careful study of several important procedures. Topics include 1- and 2-sample location problems, the one-way analysis of variance, and simple linear regression. Most assignments involve applying probability models and/or statistical methods to practical situations and/or actual data sets.

At the end of this course, students will be able to

• Characterize uncertainty and variation using probability
• Summarize data using computer graphics and numerical measures of center, spread, and
• association
• Assess whether observed data fits a probability model and understand the implications for
• analysis
• Explain what significance probabilities (P-value) and confidence intervals mean, and identify
• common misinterpretations
• Compare two or more samples or sets of measurements to draw scientific conclusions
• Apply statistical models to real data and recognize their uses and limitations

Term(s) offered: Spring, Summer

This course is a survey of statistical methods that do not rely on parametric assumptions. Knowledge of introductory statistics at the level of S320/S520 is assumed; this course is in some ways a sequel. As such, it will review the parametric techniques learned in that and similar introductory courses, and compare them to nonparametric alternatives to see when one technique outperforms another. The course material will include:

• EDA and basic concepts
• Nonparametric tests
• Empirical distributions and the bootstrap
• Multiple linear regression
• Nonparametric and penalized regression
• GLMs and other advanced models

Term(s) offered: Fall, Summer

Application of statistical analysis to issues in public and environmental affairs and related fields. Addresses descriptive statistics, statistical inference, the nature of random variables, sampling distributions, point and interval estimation of parameters (mean, standard deviation, etc.), hypothesis testing, analysis of variance, and bivariate and multivariate regression. Emphasizes practical aspects of applying such methods, appropriately interpreting the results of these statistical analysis tools, and gaining a meaningful understanding of how statistical analysis can be misused or erroneously executed. Use of computer tools for carrying out statistical analysis (primarily SAS) will is also a major emphasis

Term(s) offered: Spring

Intermediate-level perspective on statistical concepts and techniques for analyzing and modeling complex systems via regression analysis. Includes estimating the parameters of such models based on existing data, testing hypotheses about these systems, forecasting, correcting for violations of assumptions, and dealing with commonly encountered problems such as near multcollinearity. Primarily focused on single equation regression models and the extension of these models to a variety of situations, but includes an introduction to simultaneous equation models. Application of these techniques to problems and policies in public and environmental affairs, as well as general social sciences.