Recursive methods in economic dynamics pdf download

Different levels of automation are discussed, as well as the allocation of roles and authority between humans and machines. Human-vehicle interface design in highly automated systems. Decision aiding. Trade-offs between human control and human monitoring. Automated alerting systems and human intervention in automatic operation. Enhanced human interface technologies such as virtual presence.

Performance, optimization, and social implications of the human-automation system. Examples from aerospace, ground, and undersea vehicles, robotics, and industrial systems. Same subject as HST. Fundamentals of human performance, physiology, and life support impacting engineering design and aerospace systems. Case studies of current research are presented.

Assignments include a design project, quantitative homework sets, and quizzes emphasizing engineering and systems aspects. See description under subject HST. Same subject as STS. Examines concepts and procedures for new venture creation in aerospace and mobility systems, and other arenas where safety, regulation, and infrastructure are significant components.

Includes space systems, aviation, autonomous vehicles, urban aerial mobility, transit, and similar arenas. Includes preparation for entrepreneurship, founders' dilemmas, venture finance, financial modeling and unit economics, fundraising and pitching, recruiting, problem definition, organizational creation, value proposition, go-to-market, and product development.

Includes team-based final projects on problem definition, technical innovation, and pitch preparation. Fundamentals of digital signal processing with emphasis on problems in biomedical research and clinical medicine. Basic principles and algorithms for processing both deterministic and random signals.

Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. Lab projects, performed in MATLAB, provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs. Greenberg, E. Adalsteinsson, W. Prereq: None G Spring Not offered regularly; consult department units Can be repeated for credit.

Each term, the class selects a new set of professional journal articles on bioengineering topics of current research interest. Some papers are chosen because of particular content, others are selected because they illustrate important points of methodology. Each week, one student leads the discussion, evaluating the strengths, weaknesses, and importance of each paper. Subject may be repeated for credit a maximum of four terms.

Letter grade given in the last term applies to all accumulated units of Statistically based experimental design inclusive of forming hypotheses, planning and conducting experiments, analyzing data, and interpreting and communicating results. Topics include descriptive statistics, statistical inference, hypothesis testing, parametric and nonparametric statistical analyses, factorial ANOVA, randomized block designs, MANOVA, linear regression, repeated measures models, and application of statistical software packages.

Prereq: None G Fall Not offered regularly; consult department units. Provides guidance on design and evaluation of human-computer interfaces for students with active research projects. Roundtable discussion on developing user requirements, human-centered design principles, and testing and evaluating methodologies.

Students present their work and evaluate each other's projects. Readings complement specific focus areas. Team participation encouraged. Open to advanced undergraduates. Covers the mathematical foundations and state-of-the-art implementations of algorithms for vision-based navigation of autonomous vehicles e.

Topics include geometric control, 3D vision, visual-inertial navigation, place recognition, and simultaneous localization and mapping. Provides students with a rigorous but pragmatic overview of differential geometry and optimization on manifolds and knowledge of the fundamentals of 2-view and multi-view geometric vision for real-time motion estimation, calibration, localization, and mapping.

The theoretical foundations are complemented with hands-on labs based on state-of-the-art mini race car and drone platforms. Culminates in a critical review of recent advances in the field and a team project aimed at advancing the state-of-the-art.

Presents aerospace propulsive devices as systems, with functional requirements and engineering and environmental limitations. Requirements and limitations that constrain design choices. Both air-breathing and rocket engines covered, at a level which enables rational integration of the propulsive system into an overall vehicle design. Mission analysis, fundamental performance relations, and exemplary design solutions presented.

Performance and characteristics of aircraft jet engines and industrial gas turbines, as determined by thermodynamic and fluid mechanic behavior of engine components: inlets, compressors, combustors, turbines, and nozzles. Discusses various engine types, including advanced turbofan configurations, limitations imposed by material properties and stresses. Emphasizes future design trends including reduction of noise, pollutant formation, fuel consumption, and weight. Chemical rocket propulsion systems for launch, orbital, and interplanetary flight.

Modeling of solid, liquid-bipropellant, and hybrid rocket engines. Thermochemistry, prediction of specific impulse. Nozzle flows including real gas and kinetic effects. Structural constraints. Propellant feed systems, turbopumps.

Combustion processes in solid, liquid, and hybrid rockets. Cooling; heat sink, ablative, and regenerative. Prereq: 8. Reviews rocket propulsion fundamentals. Discusses advanced concepts in space propulsion with emphasis on high-specific impulse electric engines. Topics include advanced mission analysis; the physics and engineering of electrothermal, electrostatic, and electromagnetic schemes for accelerating propellant; and orbital mechanics for the analysis of continuous thrust trajectories.

Requires a term project in which students design, build, and test an electric propulsion thruster in the laboratory. Internal fluid motions in turbomachines, propulsion systems, ducts and channels, and other fluid machinery. Useful basic ideas, fundamentals of rotational flows, loss sources and loss accounting in fluid devices, unsteady internal flow and flow instability, flow in rotating passages, swirling flow, generation of streamwise vorticity and three-dimensional flow, non-uniform flow in fluid components.

Properties and behavior of low-temperature plasmas for energy conversion, plasma propulsion, and gas lasers. Equilibrium of ionized gases: energy states, statistical mechanics, and relationship to thermodynamics. Kinetic theory: motion of charged particles, distribution function, collisions, characteristic lengths and times, cross sections, and transport properties.

Gas surface interactions: thermionic emission, sheaths, and probe theory. Radiation in plasmas and diagnostics. Engineering School-Wide Elective Subject. Offered under: 1. EPE , 2. EPE , 3. EPE , 6. EPE , 8. EPE , EPE Prereq: 2. EPW , 2. EPW , 3. EPW , 6. EPW , Basic undergraduate topics not offered in regularly scheduled subjects.

Subject to approval of faculty in charge. Prior approval required. Opportunity for study or lab work related to aeronautics and astronautics not covered in regularly scheduled subjects. Opportunity for study or lab work related to aeronautics and astronautics but not covered in regularly scheduled subjects. Prereq: None. Coreq: First in a two-term sequence that addresses the conception and design of a student-defined or selected experimental research project carried out by two-person team under faculty advisement.

Principles of research hypothesis formulation and assessment, experimental measurements and error analysis, and effective report writing and oral presentation, with instruction both in-class and on an individual and team basis. Selection and detailed planning of a research project, including in-depth design of experimental procedure that is then carried through to completion in Execution of research project experiments based on the plan developed in Working with their faculty advisor and course staff, student teams construct their experiment, carry out measurements of the relevant phenomena, analyze the data, and then apply the results to assess the research hypothesis.

Includes instruction on effective report writing and oral presentations culminating in a written final report and formal oral presentation. Hall, J. Craig, P. Lozano, S. Introduces the concepts of system safety and how to analyze and design safer systems. Topics include the causes of accidents in general, and recent major accidents in particular; hazard analysis, safety-driven design techniques; design of human-automation interaction; integrating safety into the system engineering process; and managing and operating safety-critical systems.

Coreq: 2. Students should have concurrent or prior programming experience. Prereq: None U Fall units. Project-based seminar provides instruction on how to program basic autonomy algorithms for a micro aerial vehicle equipped with a camera.

Begins by introducing the constituent hardware and components of a quadrotor drone. As this subject progresses, the students practice using simple signal processing, state estimation, control, and computer vision algorithms for mobile robotics.

Students program the micro aerial vehicle to compete in a variety of challenges. Provides a foundation for students taking Through a set of focused activities, students determine the autonomous system they will design, which includes outlining the materials, facilities, and resources they need to create the system.

Opportunity to see aeronautical theory applied in real-world environment of flight. Students assist in design and execution of simple engineering flight experiments in light aircraft. Typical investigations include determination of stability derivatives, verification of performance specifications, and measurement of navigation system characteristics.

Restricted to students in Aeronautics and Astronautics. See description under subject 5. Limited to Offered under: 6.

Coreq: 6. Preference to students enrolled in the Bernard M. Preference to first-year students in the Gordon Engineering Leadership Program. Offered under: 2. Restricted to juniors and seniors. Prereq: None U Fall; first half of term units. Preference to Course 16 majors. Enrollment limited to 25; priority to first-year students.

Preference to students in the Bernard M. Feiler, L. McGonagle, R. Enrollment limited to seating capacity of classroom. Admittance may be controlled by lottery. See description under subject Opportunity to work on projects related to aerospace engineering outside the department.

Opportunity for study or laboratory project work not available elsewhere in the curriculum. Topics selected in consultation with the instructor. Study by qualified students. Speakers from campus and industry discuss current activities and advances in aeronautics and astronautics. Restricted to Course 16 students. For Course 16 students participating in curriculum-related off-campus experiences in aerospace engineering and related areas. Before enrolling, a student must have an offer from a company or organization; must identify an appropriate supervisor in the AeroAstro department who, along with the off-campus supervisor, evaluate the student's performance; and must receive prior approval from the AeroAstro department.

At the conclusion of the training, the student submits a substantive final report for review and approval by the MIT supervisor. Can be taken for up to 3 units. Contact the AeroAstro Undergraduate Office for details on procedures and restrictions. See description under subject STS. Overview of the global airline industry, focusing on recent industry performance, current issues and challenges for the future. Fundamentals of airline industry structure, airline economics, operations planning, safety, labor relations, airports and air traffic control, marketing, and competitive strategies, with an emphasis on the interrelationships among major industry stakeholders.

This course introduces the concept of human capital, its accumulation process, its role in family decisions, and its impact on the economy. Several models are presented and discussed, covering a wide range of topics, including parental altruism, education, bequests, health, fertility, support in old age, income inequality, intergenerational transmission of wealth, specialization, division of labor, and economic growth. The theory is complemented with historical evidence from different countries and periods.

The concept of "market distortion" is used to formulate measurements, explanations, and consequences of government activities including tax systems, expenditure programs, and regulatory arrangements. Topics include cross-country comparisons of government behavior, predicting microlevel responses to policy, measuring and evaluating the incidence of government activity, alternative models of government decision-making, and the application of public finance to other economics fields.

Behavioral Economics and Policy. The standard theory of rational choice exhibits explanatory power in a vast range of circumstances, including such disparate decision making environments as whether to commit a crime, have children, or seek to emigrate. Nonetheless, shortfalls from full rationality seem not to be uncommon, and are themselves, to some extent, systematic.

Behavioral economics documents and tries to account for these departures from full rationality. This course looks at areas in which some modification of the traditional rational choice apparatus might most be warranted; these include decisions that unfold over time, involve low probability events, or implicate willpower.

To what extent should public policy respond to shortfalls from rationality or concern itself with promoting happiness? Health Economics and Public Policy. This course analyzes the economics of health and medical care in the United States with particular attention to the role of government. The first part of the course examines the demand for health and medical and the structure and the consequences of public and private insurance.

The second part of the course examines the supply of medical care, including professional training, specialization and compensation, hospital competition, and finance and the determinants and consequences of technological change in medicine. The course concludes with an examination of recent proposals and initiatives for health care reform. This course extends the analysis from ECON , with a focus on understanding the way firms make decisions and the effects of those decisions on market outcomes and welfare.

The course examines the structure and behavior of firms within industries. Topics include oligopolistic behavior, the problems of regulating highly concentrated industries, and the implementation of U. This course offers an introduction to the experimental methodology while at the same time providing the students with up-to-date insights and findings on how to run an organization and how to manage a workforce.

Students will learn the basics of the experimental methodology, learn about the most ground-breaking findings in experimental economics related to the functioning of firms, and know the relevant papers and findings in organizational and personnel economics with a particular emphasis on the question of how to set incentives for workers. This is a course in microeconomics that applies traditional product and factor market theory and quantitative analysis to contemporary economic issues in professional and college athletics.

Topics include the sports business; market structures and outcomes; the market for franchises; barriers to entry, rival leagues, and expansion; cooperative, competitive, and collusive behavior among participants; labor markets, productivity, and compensation of players; racial discrimination; public policies and antitrust legislation; and financing of stadiums.

This course involves the application of the choice theory of economics to the opportunities obtainable within different legal environments.

The likelihood that a person will choose to return a lost wallet, keep a promise, drive more carefully, or heed the terms in a will is partly a function of the applicable laws and regulations. Alternative rules, under the standard Law and Economics approach, are compared in terms of the economic efficiency of their subsequent outcomes.

This efficiency lens of Law and Economics is applied to rules concerning property, torts, contracts, and criminal behavior. The economic system prevailing in most of the world today differs greatly from the idealist version of free markets generally taught in economic classes.

This course analyzes the role played by corporate governance, wealth inequality, regulation, the media, and the political process in general in producing these deviations.

It will explain why crony capitalism prevails in most of the world and why it is becoming more entrenched also in the United States of America. The course, which requires only basic knowledge of economics, welcomes undergraduates.

Enrollments for all students will be processed in timestamp order starting March 2. Students will be emailed if they are enrolled into or waitlisted for the course. The form will remain open through week 1, and will therefore act as the waitlist for the course. The deadline for enrollment processing will be the end of week 1 of spring quarter. This course uses theoretical and empirical economic tools to analyze a wide range of issues related to criminal behavior.

Topics include the police, prisons, gang behavior, guns, drugs, capital punishment, labor markets and the macroeconomy, and income inequality. We emphasize the analysis of the optimal role for public policy. Undergraduate Reading and Research.

Prerequisite s : Consent of directors of the undergraduate program. Undergraduate Honors Workshop. Yoshida, V. This course will provide an introduction to social choice, two-sided matching, house allocation, school choice, and the recent theoretical developments in kidney exchange. We will develop formal, mathematical language to evaluate and compare different mechanisms including deferred acceptance, top trading cycles, the probabilistic serial mechanism and others.

Our approach will be axiomatic; we will explore the tradeoff between the efficiency, incentive compatibility and fairness in the design of mechanisms. This course will be proof-based, so is appropriate for advanced students acquainted with formal mathematical reasoning. ECON or may be used as an economics elective, but only one may be used toward degree requirements.

This course provides a formal introduction to game theory with applications in economics. We will study models of how individuals make decisions, and how those decisions are shaped by strategic concerns and uncertainty about the world. The topics will include the theory of individual choice, games of complete and incomplete information, and equilibrium concepts such as Nash equilibrium.

The applications will include oligopoly, auctions, and bargaining. The course is appropriate for advanced undergraduates who are interested in a rigorous mathematical approach to understanding human behavior. Instructor s : B. We continue the formal introduction to decision theory and game theory begun in ECMA , with a specific focus on models of incomplete information.

Topics covered include subjective expected utility, Bayesian games, contract theory, and mechanism design. Among the applications we will consider are auctions, collusion, entry deterrence, and strategic communication. The course is appropriate for advanced undergraduates who are interested in a rigorous mathematical approach to decision making in strategic situations.

In part, this course covers the analysis of the standard auction formats i. We introduce both independent private-value models and interdependent-value models with affiliated signals. Multi-unit auctions are also analyzed with an emphasis on Vickrey's auction and its extension to the interdependent-value setting. ECMA Introduction to Empirical Analysis. This course introduces students to the key tools of econometric analysis: Probability theory, including probability spaces, random variables, distributions and conditional expectation; Asymptotic theory, including convergence in probability, convergence in distribution, continuous mapping theorems, laws of large numbers, central limit theorems and the delta method; Estimation and inference, including finite sample and asymptotic statistical properties of estimators, confidence intervals and hypothesis testing; Applications to linear models, including properties of ordinary least squares, maximum likelihood and instrumental variables estimators; Non-linear models.

Assignments will include both theoretical questions and problems involving data. Necessary tools from linear algebra and statistics will be reviewed as needed. Introduction to Empirical Analysis II. This course is an introduction to applied econometrics and builds on tools studied in ECMA Topics include: Selection on observables, instrumental variables, time series, panel data, discrete choice models, regression discontinuity, nonparametric regression, quantile regression.

Undergraduates who have taken Econ are encouraged to obtain instructor consent for enrollment. This course focuses on micro-econometric methods that have applications to a wide range of economic questions. We study identification, estimation, and inference in both parametric and non-parametric models and consider aspects such as consistency, bias and variance of estimators.

We discuss how repeated measurements can help with problems related to unobserved heterogeneity and measurement error, and how they can be applied to panel and network data.

Topics include duration models, regressions with a large number of covariates, non-parametric regressions, and dynamic discrete choice models. Applications include labor questions such as labor supply, wage inequality decompositions and matching between workers and firms. Students will be expected to solve programming assignment in R.

Instructor s : T. Applications of Econometric and Data Science Methods. This course builds on the theoretical foundations set in Econ and explores further topics pertinent to modern economic applications. The course will involve analytically and computationally intensive assignments and a significant empirical project component.

Econometrics and Machine Learning. This course reviews a number of modern methods from econometrics, statistics and machine learning, and presents applications to economic problems. Examples of methods covered are simulation-based techniques, regularization via coefficient and matrix penalization, and regression and classification methods such as trees, forests and neural networks. Applications include economic models of network formation, and dimension reduction for structural economic models.

The course involves programming and work with data. Beyond econometric background such as Econ , students should have a solid background in computation. The goal of the class is to learn how to apply microeconomic concepts to large and complex datasets. We will first revisit notions such as identification, inference and latent heterogeneity in classical contexts. We will then study potential concerns in the presence of a large number of parameters in order to understand over-fitting. Throughout the class, emphasis will be put on project-driven computational exercises involving large datasets.

We will learn how to efficiently process and visualize such data using state of the art tools in python. Topics will include fitting models using Tensor-Flow and neural nets, creating event studies using pandas, solving large-scale SVDs, etc. Introduction to Advanced Macroeconomic Analysis. This course introduces students to advanced methods for macroeconomic analysis. In the first part, we discuss time series methods such as impulse response analysis, vector autoregression, co-integration, shock identification, and business cycle detrending.

In the second part, we examine and analyze a simple, yet powerful stochastic dynamic real business cycle model. In that context, the students will learn about dynamic programming, rational expectations, intertemporal optimization, asset pricing, the Frisch elasticity of labor supply, log-linearization, and computational tools to solve for the recursive law of motion of dynamic stochastic general equilibrium models.

The course is useful for students interested to deepen their knowledge in macroeconomics, in order to read, understand, and replicate some of the recent research in the field; as preparation for careers involving macroeconomic analysis, time series analysis, or asset pricing; or as preparation for graduate school.

Decent knowledge of linear algebra and calculus is required. All advanced material will be taught in class. This course introduces students to economic theories of "crises" or particular periods of rapid negative changes in real and financial variables that are distinct from long-run growth and regular business cycles.

In particular, we will cover the origin of various types of financial crises, i. Time permitting, we will also study currency crises and speculative attacks. Throughout, our focus will be on the implications for fiscal and monetary policy. Introduction to Dynamic Economic Modeling. This course provides an introduction to dynamic economic models, with applications to macroeconomics, labor economics, financial economics, and other subfields of economics.

The core methodology will be consistent over time, but the applications will vary from year to year. The course will analyze decentralized equilibrium and social planner's problems in dynamic environments. It will focus on developing techniques for analyzing such models graphically, analytically, and computationally. Students should be familiar with constrained optimization e.

Lagrangians , linear algebra, and difference equations, as well as microeconomics, macroeconomics, and econometrics at an intermediate level. Introduction to Heterogeneous Agent Macroeconomics. This class is an introduction to macroeconomics with heterogeneous households. We will study consumption-savings problems, income dynamics, wealth inequality in partial and general equilibrium, and the effects of fiscal and monetary policy in the presence of household inequality.

The class will make use of theoretical analysis, empirical analysis and computational methods. Material will be presented in both discrete and continuous time.

Students will analyze micro-level data on wealth, income and consumption, and will learn how to write code to solve heterogeneous agent models on a computer. Familiarity with a statisical package such as R or Stata, and a programming language such as Matlab, Python, Julia, Fortran or C is highly recommended. This course is designed for those who plan to run a randomized control trial. It provides practical advice about the trade-offs researchers face when selecting topics to study, the type of randomization technique to use, the content of a survey instruments, analytical techniques and much more.

How do you choose the right minimum detectable effect size for estimating the sample size needed to run a high quality RCT? How do you quantify difficult to measure outcomes such as women's empowerment or ensure people are providing truthful answers when you are asking questions on sensitive topics like sexual health? When should you tie your hands by pre-committing to your analysis plan in advance, and when is a pre-analysis plan not a good idea?

This course will draw on lots of examples from RCTs around the world, most though not all from a development context. Alongside field tips, it will also cover the concepts and theory behind the tradeoffs researchers face running RCTs. The course is designed for PhD students but given its practical nature is open to and accessible to masters students who plan to work on RCTs. Instructor s : Glennerster, R. This course explores economic models of the demand for and supply of different forms of schooling.

The course examines the markets for primary, secondary, and post-secondary schooling. The course examines numerous public policy questions, such as the role of government in funding or subsidizing education, the design of public accountability systems, the design of systems that deliver publicly funded and possibly provided education, and the relationship between education markets and housing markets.

Empirical Industrial Organization. This course will provide an introduction to state-of-the-art methodologies in Empirical Industrial Organization. We will use real-life data to learn about consumers and firms. We will cover demand and preference estimation, production function estimation, empirical models of market entry, and auctions. We will also discuss applications including prediction, policy analysis, and price optimization.

Students will learn about theory, estimation, optimization, and practical considerations. Students will apply what they learn using R. The new reality is that every company is a software company. Even in traditionally brick-and-mortar industries, software is performing more and more of the work. Many companies especially "lean startups" are purely software-based.

Lacking an understanding of how software works and how software is built puts you at a disadvantage. Our goal is to develop an understanding of both. We believe the best way to do that is to build something yourself, using modern languages and workflows. You will build a functional prototype of your own app idea, and will learn the Ruby on Rails web application framework.

Higher-level goals are to: 1. Understand the general, platform-independent patterns of how apps work. Communicate more effectively and credibly. Develop a builder's eye for problems that can be solved with technology. Prioritize features more intelligently by developing a better feel for their costs.

Implement a modern software development workflow, from task management to version control to quality assurance to deployment. Be able to make and test small changes to an app yourself. This course is entirely project-driven. We will build a series of apps in class. Also, you will build your own app idea which will be your final project.

This course is designed for a beginner who has never programmed before. Note: Due to the intensive support requirements and volume of requests, we can't allow auditors.

Booth Book Fee may be assessed. All first year college students are restricted from enrolling into this course. BUSN and BUSN cannot count toward the standard economics major electives or the business economics specialization electives. This course provides an introduction to financial statements and the financial reporting process from a user's perspective. The focus of the course is on fundamental accounting concepts and principles. Students learn how the economic transactions of a firm are reported in the financial statements and related disclosures.

The objective of the course is to provide students with basic skills necessary to read and analyze financial statements as well as to prepare students for more advanced financial statement analysis courses. This course focuses on internal operations, cost analysis, and performance evaluation, as opposed to the evaluation of external financial statements.

Its targeted audience includes students intending become management consultants, entrepreneurs, managers e. Topics covered include overhead allocation, activity based costing, opportunity cost of excess capacity, customer profitability, capital budgeting, transfer pricing, performance evaluation, risk management, internal controls, and fraud.

Applications cover both the manufacturing and services sectors. Prerequisite s : This course is not open to MBA students. BUSN Accounting and Financial Analysis. The course is designed to provide the tools necessary to conduct a reasonably sophisticated financial statement analysis.

The focus is on the use of financial statements, although this requires some understanding of the process by which financial statements are produced. We will not limit our study to the financial statements per se. We will also work with supplemental disclosures, which help the analyst to interpret the financial statements and to understand better the economic transactions that gave rise to them. The techniques we will employ will be useful for both equity and credit analysis. Although this course does not cover forecasting or valuation per se, a thorough understanding of financial reporting issues is critical to being able to do a thoughtful financial forecast and valuation.

Specific topics include basic concepts of financial statement analysis, revenue recognition, leasing, financial analysis when there is discontinuity acquisitions, divestitures, accounting changes , accounting for income taxes, earnings per share. Other topics may be included as well. This course teaches you how to analyze financial statements in order to develop financial statement models, assess credit risk, and, ultimately, value a company. The course provides both a framework and the tools necessary to analyze financial statements.

Its primary objective is to advance your understanding of how financial reporting can be used in a variety of decisions e. It is applied in nature and stresses the use of actual financial statements. Throughout the course, I draw heavily on real business examples and use cases to illustrate the application of the techniques and tools.

Topics include traditional ratio analysis techniques, accounting analysis i. The second part of the course focuses on equity valuation, e. While students with a multitude of interests will benefit from this course, students with an interest in investment banking, equity or credit analysis, consulting, strategy, corporate finance, or management will find this course particularly relevant.

This course is intended for students who are interested in starting new entrepreneurial businesses. It is tactical, hands-on, and covers the nuts and bolts of starting a company with a lesser emphasis on investing in entrepreneurial ventures.

Students will learn how to raise seed funding, compensate for limited human and financial resources, establish brand values and positioning, secure a strong niche position, determine appropriate sourcing and sales channels, and develop execution plans in sales, marketing, product development and operations. The emphasis is managerial and entrepreneurial, essentially a working model for starting an enterprise.

This class is executed through a combination of lectures, group assignments based on student's new venture ideas, case discussions, VC and entrepreneur guest lectures and panels, and ultimately ties together in a pitch at the end of the quarter to a panel of VC observers. This course is designed to guide groups of students through the new venture creation process. Students will have passed through the first round of the College New Venture Challenge, and will be developing their own original new business ideas.

Students may enter the course with ideas that are traditionally for-profit in nature or more socially oriented either for- or not-for-profit ventures. Students must prepare and submit original feasibility summaries prior to the application deadline. During the course, students will expand these summaries into full business plans, and will be required to present their ventures multiple times to venture capital investors, entrepreneurs, and startup mentors. Students interested in careers in: startups, technology, business, consulting, and management are encouraged to take this course.

Enrollment by permission based on the feasibility summary application. This course is not open to MBA students. Consent only: Students will have passed through the first round of the College New Venture Challenge.

Social Entrepreneurship and Innovation. We will study social innovation with a focus on the role of social entrepreneurship for implementing innovative solutions to society's problems. A team of 4 students will be assigned with an innovative idea that addresses a social problem and could become a for-profit or non-profit social venture.

Introduction to Mixed Reality. Recommended: ART Project-based study and application of Mixed Reality MR topics including integrated mixed reality development environments, Human Computer Interaction HCI peripherals, 3D environment scanning, physics interaction, diminished reality, motion capture, facial recognition, and visualization hardware. Interactive Entertainment Engineering.

Project-based, software oriented, introductory study of interactive entertainment. Discussion and evaluation of classic and historically influential games. Exploration of concepts in game design and development. Topics may include interactive storytelling, game physics, game AI, character development, animation, and development of virtual worlds. Projects require significant programming.

Special Problems. Individual investigation, research, studies or surveys of selected problems. Total credit limited to 4 units. Software Requirements Engineering.

Software requirements elicitation, analysis and documentation. Team process infrastructure and resource estimation to support appropriate levels of quality. Software architectural design. Software Construction. Design and construction of sizeable software products. Technical management of software development teams. Software development process models, software design, documentation, quality assurance during development, software unit and integration testing; CASE tools, development environments, test tools, configuration management.

Senior Project - Software Deployment. Deployment of a sizeable software product by a student team. Software maintenance and deployment economic issues. Management of deployed software: version control, defect tracking and technical support.

Current Topics in Software Engineering. Selected topics in software engineering. Topics may include program generation, quality assurance, formal methods, software metrics, design methods, testing, or software development processes. Software Evaluation. Theory and practice of evaluation of software and software systems. Design of experiments for measuring software performance, measuring software output quality, comparing multiple implementations of the same algorithm, and evaluation of software heuristics.

Selection of appropriate software evaluation measures and criteria. Network Security. Prerequisite: CPE Introduction to network security, including denial of service, botnets, access control, routing attacks, transport layer attacks, tunneling mechanisms, VPNs, IDS, firewalls, penetration testing, key distribution, email security, jamming, and wireless security.

Software Security. Principles behind secure software design including threat models, trust management, common vulnerabilities and mitigation techniques, robust software development, isolation of untrusted code, auditability, and testing. Wireless Security. Comprehensive overview of wireless networks and system security.

Security issues and solutions in emerging wireless access networks and systems as well as multi-hop wireless networks. Current Topics in Computer Security. Selected topics in emerging areas of computer security. Potential topics include: network and web security, critical infrastructure protection, embedded systems security, malware analysis, mobile security, and digital forensics, among others. Programming Languages. Programming language design through evaluator implementation.

Expressions, functions, environments, closures, mutation, objects, type systems, and syntactic abstraction. Syntactic, semantic, and static analysis properties. Compiler Construction. Intermediate code representations, memory management, functions and parameter passing, code transformations and optimizations, code generation, register allocation.

Mobile Application Development. Inception, development, testing, and deployment of mobile applications. Introduction to tools, libraries, and frameworks for one or more mobile platforms and devices. Emphasis on software engineering best practices for developing entrepreneurial or humanitarian mobile-centric applications. Dynamic Web Development. Project-based study of web-based three-tiered applications, including current best practices and tools for design, implementation and testing of browser interface, serverside business logic, object-relational mapping, databases, and web services.

Theory of Computation I. Theory of formal languages and automata. Turing machines. Chomsky hierarchy. Theory of decidability and computability. Bioinformatics Algorithms. Introduction to the use of computers to solve problems in molecular biology. The algorithms, languages, and databases important in determining and analyzing nucleic and protein sequences and their structure. Team-based design, construction and deployment of a collaborative interactive computational art project typically found in the fields of animation, game design, and interactive media.

Management of inter-disciplinary teams, documentation, creative development, testing, and assessment. Introduction to Operating Systems. Implementation of Operating Systems. Design and implementation of multiprogramming kernels, systems programming methodology, interprocess communications, synchronization, device drivers and network access methods.

Current Topics in Computer Systems. Selected aspects of design, implementation and analysis of networks, advanced operating and distributed systems. Topics may include process management, virtual memory, process communication, context switching, file system designs, persistent objects, process and data migration, load balancing, security and networks.

Knowledge Discovery from Data. Overview of modern knowledge discovery from data KDD methods and technologies. Topics in data mining association rules mining, classification, clustering , information retrieval, web mining. Emphasis on use of KDD techniques in modern software applications. Database Management Systems Implementation. Data structures and algorithms used in the implementation of database systems.

Implementation of data and transaction managers: access methods interfaces, concurrency control and recovery, query processors and optimizers. Introduction to implementation of distributed database systems. Distributed Systems. Foundations of distributed systems, distributed hash tables peer-to-peer systems , failure detectors, synchronization, election, inter-process communication, consensus, replication, key-value stores, and measurements.

Introduction to Computer Graphics. Graphics software development and use of application programming interfaces for 3D graphics.

The graphics pipeline, modeling, geometric and viewing transforms, lighting and shading, rendering, interaction techniques and graphics hardware. Advanced Rendering Techniques.

Illumination models, reflectance, absorption, emittance, Gouraud shading, Phong shading, raytracing polyhedra and other modeling primitives, coherence, acceleration methods, radiosity, form factors, advanced algorithms. Computer Animation. Basic and advanced algorithms for generating sequences of synthetic images. Interpolation in time and space, procedural and keyframe animation, particle systems, dynamics and inverse kinematics, morphing and video.

Basic and advanced algorithms for real-time, interactive, 3D graphics software. Modeling polygon mesh, height field, scene graph , real-time rendering and shading visibility processing, LOD, texture and light maps , collision detection bounding volumes, complexity management , interactive controls, multi-player game technology, game engine architecture.

Powerpoint 33 Kruskal's and Prim's minimum-cost spanning tree algorithms. Powerpoint 34 Divide and conquer, and application to defective chessboard and min-max problem. Iterative min-max implementation. Powerpoint 35 Merge sort, natural merge sort, and quick sort. Powerpoint 36 Selection and closest pair of points.

Powerpoint 38 Matrix multiplication chains, dynamic programming recurrence, recursive solution. Powerpoint 39 Iterative solution to matrix multiplication chains. Powerpoint 40 All pairs shortest paths.

Powerpoint 41 Single source shortest paths with negative edge weights. Powerpoint 42 Solution space trees and backtracking. Powerpoint 43 Branch and bound. Email This BlogThis! Labels: Data Structures and Algorithms. Older Posts Home. Subscribe to: Posts Atom. Walk through of code for Chain. Stacks--application to parentheses matching, towers-of-hanoi, railroad car rearrangement, and switchbox routing; array stacks. Nonapplicability of queues for parantheses matching, towers-of-hanoi, railroad problem with LIFO tracks, and switchbox routing.

Binary tree traversal methods-- preorder, inorder, postorder, level order. Reconstruction from two orders.