BazookaCow19/class-info huggingface dataset:
IB 372: Ecology and Evolution Integrated study of ecology, population genetics, and evolution. Conceptual themes and techniques from the molecular, cellular, and organismal levels of biology will be integrated as well. Lecture, laboratory, and field work. Credit is not given for both IB 372 and either IB 203 or IB 302. Prerequisite: IB 271; good standing in the IB honors biology option.
IB 390: Undergraduate Research Experience Laboratory and/or field research and/or reading supervised by faculty members in the School of Integrative Biology. Approved for S/U grading only. May be repeated to a maximum of 10 hours. Credit is not given for more than a combined maximum of 10 hours of IB 390 or IB 490 towards graduation for IB majors. Prerequisite: Consent of instructor.
IB 401: Introduction to Entomology Integrated studies of the principal morphological, physiological, ecological and behavioral relationships among insects. Lecture and laboratory. 3 or 4 undergraduate hours. 3 or 4 graduate hours. An insect collection will be required for 4 hours credit. Prerequisite: IB 150; or consent of instructor.
IB 411: Bioinspiration Focuses on how experts in biology and technological fields find inspiration in nature and use it as a model to make technological innovations and solve societal problems. In the future, our day-to-day living, health, and the environment will benefit from interdisciplinary teams using findings in basic biological research for technological innovation. Topics to be explored include human health, efficient architecture, cooperative control, robotics, swarm logic, and advanced biological materials. 3 undergraduate hours. 3 graduate hours.
IB 426: Env and Evol Physl of Animals Physiological adaptations of invertebrate and vertebrate animals to diverse aquatic and terrestrial environments and the extreme habitats embodied therein. 3 undergraduate hours. 3 graduate hours. Prerequisite: MCB 150; IB 202; CHEM 232; or consent of instructor.
IB 430: Animal Behavior Lab Inquiry-driven laboratory course in animal behavior. Students work in groups to generate hypotheses, design experiments, collect and analyze data, and write up their results. Experiments will be carried out in both the field and lab. Discussions emphasize the scientific process, including hypothesis testing, and experimental design and statistics. 3 undergraduate hours. No graduate credit. Prerequisite: IB 329. For majors only.
IB 436: Evolutionary Neuroscience Same as NEUR 433, PHIL 433 and PSYC 433. See PSYC 433.
IB 444: Insect Ecology Discussion of the practical and theoretical aspects of ecology in relation to insects as individuals, populations, and communities; emphasis on the role of insects in the environment. 3 or 4 undergraduate hours. 3 or 4 graduate hours. Offered in alternate years. Lecture only, 3 hours; with laboratory, 4 hours. Prerequisite: IB 150 and MCB 150 or consent of instructor.
IB 452: Ecosystem Ecology Distribution and structure of ecosystems on earth; integration of multiple disciplines to gain a holistic view of ecosystem function; ecosystem concepts as they apply to understand natural and anthropogenic environmental change. Offered in alternate years. Same as ESE 452 and NRES 462. 3 undergraduate hours. 3 graduate hours. Prerequisite: CHEM 102 and CHEM 104; or consent of instructor.
IB 460: Evol of Intelligent Systems Investigates the evolution of cognitive specializations in neural systems across Eukaryota. Examines gross anatomy alongside a detailed examination of microstructure across the nervous system as it supports perceptual and decision-making processes. Lab time will provide students hands-on brain dissection, histology, and microscopy experience, while lecture will focus on explaining the mechanisms of cognition and the evolution of brain organization. Assignments enable the motivated student to develop professional skills and hone their scholarly interests. Same as NEUR 460. 4 undergraduate hours. 4 graduate hours. Prerequisite: IB 302 or consent of instructor. Restricted to Junior, Senior or Graduate students.
IB 463: Ichthyology Classification, anatomy, ecology, behavior, distribution, and evolution of fishes of the world. Emphasis is on morphological, ecological, and behavioral diversification of fishes in a phylogenetic context. Laboratory devoted to anatomy and identification. Same as NRES 463. 4 undergraduate hours. 4 graduate hours. Offered in alternate years. Prerequisite: IB 302; or consent of instructor.
IB 471: Fungal Diversity and Ecology An introduction to the extraordinary diversity of fungi: from symbionts of lichens to decomposers and mycorrhizas and the pathogens of plants and animals. The current classifications of the different groups of fungi will be discussed. Emphasis will be on practical skills used for fungal culturing and identification, and discussing the ecological role of fungi in communities and ecosystems. Offered in alternate years. 4 undergraduate hours. 4 graduate hours. Prerequisite: IB 150; IB 302 recommended; or consent of instructor.
IB 478: Advanced Plant Genetics Same as CPSC 452. See CPSC 452.
IB 479: Plant Growth and Development Same as CPSC 486. See CPSC 486.
IB 490: Independent Study Laboratory and/or field research supervised by faculty members in the School of Integrative Biology. A written report is required. 1 to 5 undergraduate hours. No graduate credit. May be repeated. Credit is not given for more than a combined maximum of 10 hours of IB 390 or IB 490 towards graduation for IB majors. Prerequisite: Consent of instructor.
IB 496: Special Courses Experimental and temporary courses. 1 to 5 undergraduate hours. 1 to 4 graduate hours. Approved for Letter and S/U grading. May be repeated in separate terms to a maximum of 8 hours, if topics vary. Prerequisite: Consent of instructor.
IB 501: Programming for Genomics Students will learn to think algorithmically by constructing a biological hypothesis, and implementing code or deploying an existing code implementation, to test that hypothesis. Students will learn to use UNIX and to program in Python, using biological data sets from high-throughput sequencing projects. We will cover major genomics approaches and the algorithms that underlie them, including K-mer analysis, genome and transcriptome assembly, databases and SQL, and visualization techniques. Same as CPSC 501. 4 graduate hours. No professional credit. Prerequisite: Courses in Ecology, Evolution, and Molecular Biology, or consent of instructor.
IB 513: Disc in Plant Physiology Approved for letter and S/U grading. May be repeated.
IB 517: Analysis of Biological Data in R Students will review and master commonly used statistical techniques including probability distributions, power analyses, t-tests, correlations, regression, ANOVA, generalized linear models, principal components analysis, model selection, and experimental design/interpretation. Weekly discussions focus on statistical issues such as data dredging, the difference between statistical and biological significance, and the difference between correlation and causation. The laboratory involves programming in R to create publication quality graphs, analyze/simulate/interpret data, and trouble-shooting code. 4 graduate hours. No professional credit. Prerequisite: General statistics course or consent of the instructor.
IB 526: Seminar in Entomology Discussions, reviews, and appraisals of special topics in the field of entomology. 0 to 1 graduate hours. No professional credit. Approved for Letter and S/U grading. May be repeated if topics vary.
IB 534: Evolution and Medicine Explores how human health is inseparably tied to our evolutionary history. Principles that apply to human health include evolutionary processes, e.g. natural selections, as well as molecular evolution, human evolution, and evolutionary-developmental biology. Explores how these principles can be applied to understand human nutrition and metabolism, reproduction, disease and stress, and behavior. These principles assist physicians, researchers, and the general public in understanding how natural selection has acted on humans over time and left us vulnerable to disease and injury. 4 graduate hours. No professional credit. Prerequisite: Restricted to OMST or Integrative Biology graduate students in master's degree programs.
IB 546: Topics in Ecology & Evolution Speaker seminar series featuring discussion, review and critical analysis of general concepts and specific problems in ecology and evolution. 1 graduate hour. No professional credit. Approved for Letter and S/U grading. May be repeated in same or separate semesters.
IB 590: Individual Topics Individual topics in research conducted under the supervision of faculty members in the School of Integrative Biology. Designed for graduate students who would like to become more familiar with specialized fields of study prior to committing themselves to a specific area for their doctorate degree. Approved for S/U grading only. May be repeated to a maximum of 16 hours. Prerequisite: Consent of instructor.
IB 592: Career and Skill Development in Integrative Biology A successful career in the sciences requires tools and skills for communicating research, such as how to master the scientific literature and how to write scientifically as informed scientists to broad audiences. This course helps to build these skills while also developing professional skills to better compete for positions in professional school, jobs in industry, non-profit, or government. Students will also explore career options available with an IB degree. This course is designed to teach students these skills with targeted in and out of class exercises. 2 graduate hours. No professional credit. Prerequisite: Restricted to graduate students within the School of Integrative Biology. This course is mandatory for MS in IB graduate students.
IE 297: Independent Study Individual investigations of any phase of Industrial Engineering. May be repeated in separate terms. Prerequisite: Consent of instructor.
IE 300: Analysis of Data Nature of probabilistic models for observed data; discrete and continuous distribution function models; inferences on universe parameters based on sample values; control charts, acceptance sampling, and measurement theory. Credit is not given towards graduation for both IE 300 and CEE 202; credit is also not given towards graduation for both IE 300 and BIOE 310. Prerequisite: MATH 241.
IE 310: Deterministic Models in Optimization Linear Optimization - Simplex method, duality, and sensitivity analysis, Transportation and Assignment Problems, Network Optimization Models, Dynamic Programming, Nonlinear optimization, and Discrete optimization. Credit is not given for both IE 310 and CEE 201. Prerequisite: Credit or concurrent registration in MATH 257 or MATH 415.
IE 360: Facilities Planning and Design Facility planning, plant layout design, and materials handling analysis; determination of facilities requirements, site selection, materials flow, use of analytical and computerized techniques including simulation, and applications to areas such as manufacturing, warehousing, and office planning. Prerequisite: Credit or concurrent enrollment in IE 310.
IE 397: Independent Study Individual investigations or studies of any phase of Industrial Engineering. May be repeated in separate terms. Prerequisite: Consent of instructor.
IE 400: Design & Anlys of Experiments Concepts and methods of design of experiments for quality design, improvement and control. Simple comparative experiments, including concepts of randomization and blocking, and analysis of variance techniques; factorial and fractional factorial designs; Taguchi's concepts and methods; second-order designs; response surface methodology. Engineering applications and case studies. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: IE 300.
IE 405: Computing for ISE Introduces students to algorithm design, computer programming in C++, and database SQL queries. Provides the fundamental methods, concepts and principles of these topics to give students enough breadth to use these techniques in their jobs and to prepare them to pursue advanced topics in these areas. There will be weekly programming assignments to implement algorithms and SQL covered in the class. 3 undergraduate hours. 4 graduate hours. Prerequisite: CS 101 or CS 124 or equivalent.
IE 410: Advanced Topics in Stochastic Processes & Applications Modeling and analysis of stochastic processes. Transient and steady-state behavior of continuous-time Markov chains; renewal processes; models of queuing systems (birth-and-death models, embedded-Markov-chain models, queuing networks); reliability models; inventory models. Familiarity with discrete-time Markov chains, Poisson processes, and birth-and-death processes is assumed. Same as CS 481. 3 undergraduate hours. 4 graduate hours. Prerequisite: IE 310.
IE 411: Optimization of Large Systems Practical methods of optimization of large-scale linear systems including extreme point algorithms, duality theory, parametric linear programming, generalized upper bounding technique, price-directive and resource-directive decomposition techniques, Lagrangian duality, Karmarkar's algorithm, applications in engineering systems, and use of state-of-the-art computer codes. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: IE 310 and MATH 257 or MATH 415.
IE 412: OR Models for Mfg Systems Operations research techniques applied to problems in manufacturing and distribution. Single and multi-stage lot sizing problems, scheduling and sequencing problems, and performance evaluation of manufacturing systems. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: IE 310.
IE 421: High Frequency Trading Technology Teaches students both the core concepts and underlying mechanics of, step by step, message by message, bit for bit, exactly how trillions of dollars in notional value are automatically traded daily around the globe, whether it is stocks, bonds, options, futures, currencies, crypto, etc. High Frequency Trading will provide students with an exciting introduction both to the modern world of automated finance and to many exciting technologies that power it. Where does the "actual" real-time price of a particular asset come from at any point in time? How exactly is it being calculated and by who or what? Is there even a single price or are there multiple, and are any of those prices actually correct? Just how fast can modern traders process market data or execute trades and how do they accomplish this? 4 undergraduate hours. 4 graduate hours. Credit is not given if student received credit in IE 498/598 Electronic Trading or IE 498/598 High Frequency Trading. Prerequisite: Should have an understanding of programming and data structures and be proficient in coding in at least one programming language (typically python, C/C++, java, javascript, etc). Students who have taken CS 225 would have the requisite knowledge, but it is not required students have taken this course.
IE 431: Design for Six Sigma Quality Engineering principles and the Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) process. Application of concepts and methods of statistical process control, designed experiments, and measurement systems analysis to cases of quality and productivity improvement; application of the fundamentals of quality engineering and the Six Sigma to areas of produce development, service enterprise, and manufacturing processes. 3 undergraduate hours. 3 graduate hours. Prerequisite: IE 300.
IE 441: Work and Organization Design In Work and Organization Design, we will learn about models and theories of work and organization design. By the end of the class you will be able to distinguish between good and bad jobs, including important characteristics of organizations, based on empiric evidence. This class will also provide tools and skills to analyze and redesign jobs to make bad jobs into good ones. We will discuss various implementation strategies to intelligently apply redesigns, which improves their success. 3 undergraduate hours. 4 graduate hours. Prerequisite: IE 340 or consent of instructor.
IE 445: Human Performance and Cognition in Context Same as EPSY 456 and PSYC 456. See EPSY 456.
IE 497: Independent Study Independent study of advanced problems related to industrial engineering. 1 to 4 undergraduate hours. 1 to 4 graduate hours. May be repeated. Prerequisite: Consent of instructor.
IE 517: Machine Learning in Finance Lab Machine Learning includes the design and the study of algorithms that can learn from experience, improve their performance and make predictions. This course is designed specifically and exclusively for MSFE first semester students. It features rigorous coding exercises in Python and acts as preparation for later courses. Students will learn the concepts behind different supervised machine learning algorithms and implement them in Python using advanced packages; pandas, NumPy, and scikit-learn. All the data for this course features unique real-world financial datasets. 2 graduate hours. No professional credit.
IE 521: Convex Optimization Finite dimensional convex optimization problems; characterization of optimal solutions; iterative algorithms for differentiable and nondifferentiable problems; distributed optimization algorithms; robust problems and solutions; applications of convex optimization models. 4 graduate hours. No professional credit. Prerequisite: ECE 490 or IE 411; MATH 416; MATH 444.
IE 522: Statistical Methods in Finance Statistical tools that are fundamental for financial modeling, analyzing financial data and further studies in financial engineering. Topics include summary statistics, statistical plots, point estimation, accuracy and precision, confidence interval, Monte Carlo simulation, maximum likelihood estimation, normal mixture, resampling, hypothesis testing, simple linear regression, multiple linear regression, variable selection, regression diagnostics, autocorrelation, moving average models, filtering, autoregressive models, ARIMA, forecasting, and selected additional topics. Implementations are done using R. 4 graduate hours. No professional credit. Credit is not given for both IE 522 and GE 524. Prerequisite: IE 300 and MATH 461.
IE 523: Financial Computing Review of C++ programming: structures, classes, I/O, C++ standard libraries, Recursion. Linear Algebra tools and MILP solvers in C++. Computational aspects of probability, statistics and simulation in C++. Methods: Root-finding, Taylor's Expansion, FFTs, Dynamic Programming, Monte Carlo Methods. Financial computing case studies in C++. 4 graduate hours. No professional credit. Prerequisite: CS 225.
IE 524: Optimization in Finance Basic optimization models, theory and methods for financial engineering including linear, quadratic, nonlinear, dynamic integer, and stochastic programming; applications to portfolio selection, index fund tracking, asset management, arbitrage detection, option pricing and risk management; optimization software for classes of optimization problems. Projects requiring building optimization models based on financial market data and solutions using optimization solvers. 2 graduate hours. No professional credit. May be repeated in the same or separate semesters if topics vary to a maximum of 4 hours. Prerequisite: FIN 500 and MATH 257 or equivalent.
IE 527: MSFE Professional Development Preparing MSFE students for successful careers in financial engineering. Topics include financial engineering career paths, MSFE concentrations and electives, preparing resumes and cover letters, interview procedures and preparation, networking, and job offer and salary negotiation. Lectures will be supplemented with seminars given by MSFE alumni, MSFE practicum project sponsors, and practitioners. They will give insights on financial markets, employment opportunities, market trends, internship and full-time job searching, and much more. 0 graduate hours. No professional credit. Approved for S/U grading only. May be repeated in separate terms. Prerequisite: Restricted to MS: Financial Engineering Students only.
IE 532: Analysis of Network Data This course will focus on statistical aspects analyzing network data. It will review illustrative problems relating to aggregation of information, decision-making, and inference tasks over various graphical models and networks. 4 graduate hours. No professional credit. Prerequisite: MATH 412. ISE graduate students and students enrolled in the Master of Science in Advanced Analytics (MCAA) are eligible to take the course.
IE 534: Deep Learning Provides an introduction to neural networks and recent advances in deep learning. Topics include training and implementation of neural networks, convolution neural networks, recurrent neural networks (LSTM and gated recurrent), residual networks, reinforcement learning, and Q-learning with neural networks. A part of the course will especially focus on recent work in deep reinforcement learning. The course will also cover deep learning libraries (e.g., Chainer, Tensorflow) and how to train neural networks using GPUs and GPU clusters. Same as CS 547. 4 graduate hours. No professional credit. Credit is not given for both IE 534 and IE 434. Prerequisite: CS 446 or equivalent. Graduate students only.
IE 590: Seminar Presentation and discussion of significant developments in industrial engineering. Approved for S/U grading only. May be repeated.
IE 597: Independent Study Independent study of advanced problems related to industrial engineering. May be repeated in the same or separate terms if topics vary to a maximum of 12 hours. Prerequisite: Consent of instructor.
IE 598: Special Topics Subject offerings of new and developing areas of knowledge in industrial engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. 1 to 4 graduate hours. No professional credit. Approved for Letter and S/U grading. May be repeated in the same or separate terms if topics vary.
IE 599: Thesis Research Approved for S/U grading only. May be repeated.
INFO 199: Undergraduate Open Seminar May be repeated in separate terms to a maximum of 6 hours. Prerequisite: Consent of instructor.
INFO 202: Social Aspects Info Tech Same as IS 202 and MACS 202. See IS 202.
INFO 303: Writing Across Media The ability to communicate effectively in multiple types of media is a crucial part of literacy in our society. In this course, students will explore the intersections of various media: print, film, images, sound, etc. Students will consider the ways in which writing--as an object and as a practice--is shaped by multimodal interactions. Also integrates practical activities with broader theoretical issues in order to provide effective strategies for designing multimedia presentations, projects, and texts that integrate photography, video, and sound. Same as WRIT 303.
INFO 326: New Media, Culture & Society Same as MACS 326. See MACS 326.
INFO 333: User Experience Design In Action Introduces you to principles and techniques via several approaches to user experience design. We’ll explore UX from different angles, including visual and sensory components, infrastructure and data, and social science evaluation and research methods. Along the way students tackle hands-on assignments that relate to tried-and-true methods, like rapid prototyping, usability or multimodal communication, as well as emergent areas such as AI-based generation or interactive data visualization. Prerequisite: Restricted to sophomore standing or above.
INFO 399: Individual Study Individual study in a subject related to informatics not covered in normal course offerings. Approved for Letter and S/U grading. May be repeated in separate terms to a maximum of 6 hours. Prerequisite: Consent of instructor.