Typically offered in Fall, Spring, Summer, Winter. Introduction to Applied Mathematics. The course is designed to help prepare students to understand almost any quantitative issues they will encounter in contemporary society.
Topics are selected from the following: principles of reasoning, problem-solving tools, financial management, exponential growth and decay, probability, putting statistics to work, mathematics and the arts, discrete mathematics in business and society and the power of numbers. Algebra and Functions.
A review of basic algebra, followed by a thorough treatment of polynomial, rational, exponential, and logarithmic functions. Successful completion of this course prepares students for MAT Algebra, Functions, and Trigonometry. Topics include polynomial, rational, exponential, logarithmic, and trigonometric functions. An emphasis is placed on using technology to understand topics of importance in the life and earth sciences. Introduction to Statistics I.
Introduction to statistics and statistical inference. Concepts include: descriptive statistics, sampling distributions, confidence intervals, hypothesis testing, along with a formal introduction to linear regression and categorical data analysis. Statistical software including, but not limited to SPSS and Excel, will be used to facilitate the understanding of important statistical ideas and for the implementation of data analysis in many areas of application.
Introduction to Statistics and Probability. Introduction to probability, statistics, and statistical inference. Concepts include: descriptive statistics, probability, probability distributions, sampling distributions, confidence intervals, hypothesis testing, along with a formal introduction to linear regression and categorical data analysis.
Statistical software, including but not limited to SPSS and Excel, will be used to facilitate the understanding of important statistical ideas and for the implementation of data analysis in many areas of application. An emphasis is placed on understanding function properties and graphs without the use of technology.
Brief Calculus. An intuitive approach to calculus with emphasis on conceptual understanding and applications to business. Topics include differentiation, curve-sketching, optimization, integration, and partial derivatives. Calculus for the Life Sciences. An overview of differential and integral calculus, motivated through biological problems. Topics include mathematical modeling with functions, limits, continuity, differentiation, optimization, and integration.
Graphing calculators are used as an aid in the application of calculus concepts and methods to realistic biological problems. Introduction to Discrete Mathematics. Set theory, Boolean logic, elementary combinatorics, proofs, simple graph theory, and simple probability. Calculus I. Differential and integral calculus of real-valued functions of a single real variable with applications. Calculus II. Continuation of MAT including the study of series, methods of integration, transcendental functions, and applications to the sciences.
Topics in Mathematics. Topics announced at time of offering. Consent: Permission of the Department required to add. The Nature of Mathematics. Topics include the role of mathematics in contemporary society, career opportunities, mathematical notation and argument, structure of proofs, basic facts about logic, mathematical proofs, problem-solving techniques, and introductions to mathematical software packages.
Course should be taken by the end of sophomore year. Calculus and Linear Algebra for Applied Statistics. This course is designed to survey concepts from calculus and linear algebra that are relevant to the study of applied statistics. Topics include a review of differentiation and the Fundamental Theorem of Calculus, techniques and applications of integration, infinite series, partial derivatives, multiple integrals, matrix operations, linear transformations, and eigenvectors.
Calculus III. Numerical methods. Boundary value problems. Perturbation and variational techniques. Diffraction: Plane waves expansions, angular spectra, 2D Fourier transform, scalar and vector diffraction theory, Fresnel and Fraunhofer diffraction, coherence. Linear optical systems: Thin lenses, Gaussian beam optics, transmission functions, linear systems theory, imaging, transfer functions, aberration.
Applications: Holography, diffractive optics, gratings, optical correlation. Principles of Optical Amplification. Steady-state model of SOA. Dynamic model of SOA. Network Applications of SOAs. SOA Nonlinearities. SOA Wavelength Converters. SOA optical gates. SOA Logic Devices. Optical Memory Devices. SOA based signal regeneration. Interference phenomena.
Shielding of conductors. Other noise reduction techniques. EMI filters. Noise sources: narrowband and broadband. Electromagnetic pulse as an interference source. Wiener processes. Stochastic Wiener-Ito integrals. Stochastic integrals with respect to Poisson measures. Stochastic differentials. Diffusion processes. Ito-stochastic differential equations: existence and uniqueness of solutions, continuous dependence of solutions to parameters.
Semigroup theory. Generation of semigroups applied to stochastic differential equations. Applications to engineering systems modelling. Random variables. Distribution and density functions.
Functions of random variables. Moments and characteristic functions. Random vectors. Sequences of random variables and convergence.
Limit theorems. Stochastic processes: basic notions. Poisson and Gaussian processes. Second order processes. Representation theorems. Markov processes and chains. Resource sharing issues: delay, throughput and queue length. Basic queueing theory, Markov chains, birth and death processes. Little's Rule. Queueing applications. Introduction, applications, standards. Networking technologies.
Image, video and audio compression. Quality of Service and resource management. Scheduling issues for real-time MM transport. Multimedia synchronization. Multimedia and the Internet. Multimedia conferencing. Multimedia to the home. Satellites and multimedia. Multimedia applications. Network performance issues and their mathematical analysis techniques.
Selected topics from current literature on various network applications. Basic concepts. Virtual worlds. Hardware and software support. World modeling. Geometric modeling. Light modeling. Kinematic and dynamic models. Other physical modeling modalities.
Multisensor data fusion, anthropomorphic avatars. Animation: modeling languages, scripts, real-time computer architectures. VE interfaces. Lossless coding: discrete sources, entropy rate, Huffman coding, arithmetic coding, dictionary methods. Lossy coding: continuous sources, rate-distortion functions. Waveform coding methods: scalar and vector quantization, predictive coding, transform coding, subband and wavelet coding.
Applications to telecommunications and storage: text, speech, audio, facsimile, image, video. Mathematical models of image formation based on the image modality and tissue properties. Linear models of image degradation and reconstruction. Inverse problems and regularization for image reconstruction.
Self-organized, mobile, and hybrid ad hoc networks. Physical, medium access, networks, transport and application layers, and cross-layering issues. Power management. Security in ad hoc networks. Topology control and maintenance. Date communication protocols, routing and broadcasting. Location service for efficient routing. Bayesian networks, factor graphs, Markov random fields, maximum a posteriori probability MAP and maximum likelihood ML principles, elimination algorithm, sum-product algorithm, decomposable and non-decomposable models, junction tree algorithm, completely observed models, iterative proportional fitting algorithm, expectation-maximization EM algorithm, iterative conditional modes algorithm, variational methods, applications.
Wireless systems and their imitations. Introduction to propagation and antenna arrays. Concept of smart antenna; spatial processing; space-division multiple access. Types of smart antennas. Range and capacity improvement. Beamforming networks and algorithms. Direction-of-arrival estimation. Multiple-input multiple- output MIMO architecture: basic principles; capacity issues; performance analysis.
Space-time coding. Alamouti scheme. Spatio-temporal radio channels. Impact of correlation. Introduction to mobile and cellular systems. Digital modulation and transmission system performance. Signal processing techniques, diversity and beamforming. Multiple-input multiple-output MIMO systems. New directions and recent results. Covers media compression, in-depth issues of scalability in the compression domain including audio, images, video, 2D and 3D graphics , and adaptation towards various contexts; also covers various popular media encoding standards including JPEG and MPEG.
Covers network routing, in-depth issues and technologies in traffic engineering, quality of service, protection for high-speed networks. Addresses the following topics: basic routing, MPLS Multiprotocol Label Switching system components and architecture, constraint-based routing, traffic engineering, content distribution networks, network monitoring and measurements, quality of service, protection and restoration, virtual private networks, cross layer interworking, and special topics.
Planning process of computer networks; needs and technical requirements; modeling of different network planning problems; exact and approximate algorithms; topological planning and expansion problems; equipment switch, router location problem; approximate and optimal routing algorithms; presentation of various case studies. Quality of service measures at different layers.
Parameter adaptation, trade-offs, and optimization at physical, data-link, network, transport, and application layers.
Examples of cross-layer design in cellular, ad hoc, sensor, local area, green, and cognitive radio networks. This course is an introduction to ubiquitous sensing systems for intelligently coordinated and efficient cities and spaces. Three primary foci will be on smart cities sensing, reliable sensory data acquisition, and security and privacy in smart city sensing systems. Topics will include: a thorough presentation of sensor and actuator networks for smart cities, software-defined Internet of Things, vehicular sensing, social sensing, detailed investigation of opportunistic and participatory sensing solutions, sensing as a service, and security and privacy assurance in smart city services by using artificial intelligence methods.
An emphasis will be given on the design and analysis of multi-purposed, non-dedicated and large-scale sensing systems along with the trustworthiness, reliability, security and efficiency requirements of smart city services. Communication fundamentals. Wireless communications. Device-to-device communications.
Cyber physical systems CPS. Supervised and unsupervised learning. Reinforcement learning. Deep learning. Robotics as the intelligent connection of perception to action. Advanced robotics technologies. Robot arm kinematics and dynamics. Planning of manipulator trajectories. Control of robot manipulators. Robot-level programming. Sensors and sensory perception. Control problems for sensory controlled robotic-based flexible manufacturing systems. Task-level programming.
Knowledge-based control for mobile robots. Introduction to Lisp and Objective C. Knowledge representation using rules, semantic nets and frames. State space representation. Procedural and declarative knowledge. Production systems. Solution searching algorithms. Expert system components.
Inference engine principle and representation. Knowledge-based system design. Case study: expert system for process control. Image acquisition. Structured light and stereo ranging. Grey-scale and binary images: geometric and topological properties. Image segmentation, preprocessing, edge finding, processing. Image recognition. Mathematical models for image representation. Representation of 3-D objects, scene understanding, motion detection.
Massively parallel computers architectures. Machine vision for manufacturing. Theory and hands-on experience of virtualization technology and infrastructure to support cloud computing systems and services starting from Metal-As-A-Service and building up to a full, open, standards compliant Software-As-A-Service stack. Full explanation of the processes, methodologies, and tools needed for DevOps support. Fundamentals of complex and large-scale data processing in the cloud evolution, characteristics, application.
Batch processing. View Financial mathematics Research Papers on Academia. Xtra Gr 12 Maths: In this lesson on Financial Maths we focus on simple and compound interest as well as depreciation. Title II. Petr Zima and Robert L. All sub-parts are weighted equally. Financial Mathematics Program Overview. Payment scheduleelaborate discussion of nancial mathematics in both discrete and continuous time we also refer to books by Shreve a, b ; students with an interest in economics are encouraged to also consult Du e and Hull Finance - Mathematical models I.
The formula for simple interest is 1. They form a small, yet well respected, influential and relatively well-paid profession. Money Math is one workbook of the Everyday Math Skills series Financial Mathematics FMT , often variously named as financial engineering, mathematical finance, computational finance, analytical finance, or quantitative finance, is one of the fascinating areas of business management studies.
Other accumulation methods 5. The Mathematics Grade 11 www. It draws on tools from probability, statistics, stochastic processes, and Financial Mathematics is an ideal area for providing a broad view of the mathematical sciences. This could be addressed through student learning and teaching by reshaping business schools to include well designed financial mathematics courses that are compulsory, in degree programs. To start, 1. This may then be successfully built upon in Grade 11, eventually culminating in the concepts of Present and Future Value Annuities in Grade Describe how a compound interest model can be used to represent the effect of investing a sum of money over a period.
It is thus assumed Financial Mathematics, First Semester. This amount is called the future value of P dollars at an interest rate r for time t in years. Financial Mathematics for Actuaries Chapter 2 Annuities. Fifth edition. ISBN None Pages: C86 Which is a better choice? Prerequisite: Recommended: Undergraduate-level knowledge of system software e.
EECS and organization of digital computers e. Problems in hardware, firmware microprogram , and software. Computer architecture for resource sharing, real-time applications, parallelism, microprogramming, and fault tolerance. Prerequisite: Recommended: Undergraduate-level knowledge of organization of digital computers e. Design and Analysis of Algorithms. Computer algorithms from a practical standpoint.
Algorithms for symbolic and numeric problems such as sorting, searching, graphs, and network flow. Analysis includes algorithm time and space complexity. Advanced Application of Algorithms. Medium-sized group and individual programming project. Prerequisite: Recommended: Undergraduate course work in engineering data structures and algorithms.
Overview of integrated fabrication, circuit simulation, basic device physics, device layout, timing; MOS logic design; layout generation, module generation, techniques for very large scale integrated circuit design. Advanced Digital Signal Processing Architecture. Study the latest DSP architectures for applications in communication wired and wireless and multimedia processing.
Emphasis given to understanding the current design techniques and to evaluate the performance, power, and application domain of the latest DSP processors. Topics in Computer Engineering. Computational models for embedded systems. System-level specification and description languages. Concepts, requirements, examples.
Embedded system models at different levels of abstraction. Modeling of test benches, design under test, IP components. Discrete event simulation, semantics, and algorithms.
Real-Time Computer Systems. Time bases, clock synchronization, real-time communication protocols, specification of requirements, task scheduling. Validation of timelines, real-time configuration management.
High-Performance Computing. Fundamentals of high-performance computing, covering both theory and practice. Embedded systems design flow and methodology. Design space exploration. Co-design of hardware and software, embedded architecture and network exploration and synthesis. Real-time constraints, specification-to-architecture mapping, design tools and methodologies.
Embedded system software concepts, requirements, examples, for engineering applications such as multi-media and automotive. Software generation methodology. Algorithmic specification, design constraints. Embedded operating systems. Static, dynamic, real-time scheduling. Code generation, compilation, instruction set simulation. Cyber-Physical System Design. Model-based design of cyber-physical systems including, e. From an inverter to server centers, low-power design theory and practice in modern systems-on-chip SoC , energy efficient design time and runtime methods are surveyed at circuit, RTL, and architecture levels.
Lab assignments will help students quantify tradeoffs and design practices. Green energy sources for production, transmission, storage, and utilization of electricity, with a special focus on solar, wind, and nuclear energy production. Study of newly developed renewable sources of energy including capital cost, product cost, environmental issues, and technical feasibility. Advanced study about system security. Topics include software vulnerabilities buffer overflow , vulnerability discovery fuzzing , network security, side-channel analysis power analysis and micro-architecture attacks , machine-learning security, IoT security, and privacy differential privacy.
Extensions of probability theory to random variables varying with time. General properties of stochastic processes. Estimation, including nonlinear and linear minimum mean square error and maximum likelihood. Spectral density and linear filters. Poisson processes and discrete-time Markov chains. Prerequisite: Recommended: Knowledge of engineering probability e.
Concepts and applications of digital communication systems. Baseband digital transmission of binary, multiamplitude, and multidimensional signals. Introduction to and performance analysis of different modulation schemes. Digital Communications I. Digital Communications II.
Concepts and applications of equalization, multi-carrier modulation, spread spectrum and CDMA. Digital communications through fading memory channels. Concepts and applications of equalization, multi-carrier modulation, spread spectrum, and CDMA.
Fundamental capabilities and limitations of information sources and information transmission systems. Analytical framework for modeling and evaluating communication systems: entropy, mutual information asymptotic equipartition property, entropy rates of a stochastic process, data compression, channel capacity, differential entropy, the Gaussian channel.
Different techniques for error correcting codes and analyzing their performance. Linear block codes; cyclic codes; convolutional codes. Minimum distance; optimal decoding; Viterbi decoding; bit error probability. Coding gain; trellis coded modulation. Introduction to wireless communications systems. Wireless channel modeling. Single carries, spread spectrum, and multi-carrier wireless modulation schemes.
Diversity techniques. Multiple-access schemes. Transceiver design and system level tradeoffs. A fundamental study of: Capacity of MIMO Channels, space-time code design criteria, space-time block codes, space-time trellis codes, differential detection for multiple antennas, spatial multiplexing, BLAST.
Storage architecture, storage network and networking algorithms in data centers, principle of storage devices and non-volatile memory, data consistency, data availability and integrity, power management.
Computer and Communication Networks. Network architecture of the Internet, telephone networks, cable networks, and cell phone networks. Network performance models. Advanced concepts and implementations of flow and congestion control, addressing, internetworking, forwarding, routing, multiple access, streaming, and quality-of-service.
Digital Signal Processing I. Fundamental principles of digital signal processing, sampling, decimation and interpolation, discrete Fourier transforms and FFT algorithms, transversal and recursive filters, discrete random processes, and finite-word effects in digital filters. Prerequisite: Recommended: Knowledge of digital signal processing e. Detection, Estimation, and Demodulation Theory. Fundamentals of hypothesis testing and Bayes and Maximum Likelihood Estimation.
ARMA and state variable models for random time series analysis. Wiener and Kalman filtering and prediction. Adaptive algorithms for identification and tracking of parameters of time-varying models. State-space representation of continuous-time and discrete-time linear systems. Controllability, observability, stability. Topics include naming shared data, synchronizing threads, and the latency and bandwidth associated with communication.
Case studies on shared-memory, message-passing, data-parallel and dataflow machines will be used to illustrate these techniques and tradeoffs. Programming assignments will be performed on one or more commercial multiprocessors, and there will be a significant course project. This course takes a multi-disciplinary perspective of information security and privacy, looking at technologies as well as business, legal, policy and usability issues.
The objective is to prepare students to identify and address critical security and privacy issues involved in the design, development and deployment of robust computer and information systems. Examples used to introduce concepts covered in the class range from enterprise systems to mobile computing, the Internet of Things, social networking and digital currencies. Sound, e.
In this course we will cover the basics of Digital Signal Processing. We will concentrate on the basic mathematical formulations, rather than in-depth implementation details. We will cover the breadth of topics, beginning with the basics of signals and their representations, the theory of sampling, important transform representations, key processing techniques, and spectral estimation.
Cars, aircraft, and robots are prime examples, because they move physically in space in a way that is determined by discrete computerized control algorithms.
Designing these algorithms to control CPSs is challenging due to their tight coupling with physical behavior. At the same time, it is vital that these algorithms be correct, since we rely on CPSs for safety-critical tasks like keeping aircraft from colliding.
This course pursues the fundamental question: "How can we provide people with cyber-physical systems they can bet their lives on? We will study the fundamental architectural elements of programming web sites that produce content dynamically. The primary technology introduced will be the Django framework for Python, but we will cover related topics as necessary so that students can build significant applications.
Students must have programming and software design experience equivalent to about a typical Junior CS major and ;-a sequence of three college CS courses or more. Python-specific experience is not necessary. Students must provide their own computer hardware for this course.
Please see the Related URL above for more information. We focus on the cryptographic and mathematical foundations of Blockchains. The course will start from the basics and will cover the latest research in this area towards the end. Second, for students to gain practical experience designing, implementing, and debugging real distributed systems. The major themes this course will teach include scarcity, scheduling, concurrency and concurrent programming, naming, abstraction and modularity, imperfect communication and other types of failure, protection from accidental and malicious harm, optimism, and the use of instrumentation and monitoring and debugging tools in problem solving.
As the creation and management of software systems is a fundamental goal of any undergraduate systems course, students will design, implement, and debug large programming projects.
As a consequence, competency in both the C and Java programming languages is required. To make the issues more concrete, the class includes several multi-week projects requiring significant design and implementation.
The goal is for students to learn not only what computer networks are and how they work today, but also why they are designed the way they are and how they are likely to evolve in the future. We will draw examples primarily from the Internet. Case studies on open-source and commercial database systems will be used to illustrate these techniques and trade-offs. The course is appropriate for students with strong systems programming skills.
We study specific algorithms for a variety of problems, as well as general design and analysis techniques.
Specific topics include searching, sorting, algorithms for graph problems, efficient data structures, lower bounds and NP-completeness. A variety of other topics may be covered at the discretion of the instructor. These include parallel algorithms, randomized algorithms, geometric algorithms, low level techniques for efficient programming, cryptography, and cryptographic protocols.
In a standard algorithms course, one concentrates on giving resource efficient methods to solve interesting problems. In this course, we concentrate on techniques that prove or suggest that there are no efficient methods to solve many important problems.
We will study techniques to classify problems according to our available taxonomy. By developing a subtle pattern of reductions between classes we will suggest an as yet unproven! What does it even mean? This course takes the ideas of a traditional algorithms course, sorting, searching, selecting, graphs, and optimization, and extends them to problems on geometric inputs.
We will cover many classical geometric constructions and novel algorithmic methods. Some of the topics to be covered are convex hulls, Delaunay triangulations, graph drawing, point location, geometric medians, polytopes, configuration spaces, linear programming, and others. This course is a natural extension to , for those who want to learn about algorithmic problems in higher dimensions. Topics vary from semester to semester. This course teaches students how to think about three-dimensional shape, both mathematically and computationally.
Students will get a crash course in differential geometry, and will apply this knowledge to design and implement practical algorithms for 3D geometry processing.
Basic geometric concepts like curvature are examined via complementary computational and mathematical points of view, with an emphasis on visual intution and real-world applications. In homework, students will derive and implement core geometry processing algorithms; they will also explore a topic of their choice in a final class project. MS and PhD students will complete additional written and coding exercises, and will perform a more comprehensive literature review for their final project.
Topics include curves and surfaces, curvature, connections and parallel transport, exterior calculus, simplicial homology, conformal mapping, finite element methods, and numerical linear algebra; applications include approximation of curvature, curve and surface smoothing, surface parameterization, vector field design, and computation of geodesic distance.
Topics covered include basic image processing, geometric transformations, geometric modeling of curves and surfaces, animation, 3-D viewing, visibility algorithms, shading, and ray tracing. Its role is to overcome the limitations of the traditional camera, by combining imaging and computation to enable new and enhanced ways of capturing, representing, and interacting with the physical world.
This advanced undergraduate course provides a comprehensive overview of the state of the art in computational photography. At the start of the course, we will study modern image processing pipelines, including those encountered on mobile phone and DSLR cameras, and advanced image and video editing algorithms.
Near the end of the course, we will discuss active research topics, such as creating cameras that capture video at the speed of light, cameras that look around walls, or cameras that can see through tissue.
The course has a strong hands-on component, in the form of seven homework assignments and a final project. In the homework assignments, students will have the opportunity to implement many of the techniques covered in the class, by both acquiring their own images of indoor and outdoor scenes and developing the computational tools needed to extract information from them. For their final projects, students will have the choice to use modern sensors provided by the instructors lightfield cameras, time-of-flight cameras, depth sensors, structured light systems, etc.
This course requires familarity with linear algebra, calculus, programming, and doing computations with images. The course does not require prior experience with photography or imaging.
The course also includes a brief overview of story-boarding, scene composition, lighting and sound track generation. The second half of the course will explore current research topics in computer animation such as dynamic simulation of flexible and rigid objects,automatically generated control systems, and evolution of behaviors.
The course should be appropriate for graduate students in all areas and for advanced undergraduates. Faculty and teaching assistants from computer science and art teach the class as a team. It is a project-based course in which four to five interdisciplinary teams of students produce animations. Most of the animations have a substantive technical component and the students are challenged to consider innovation with content to be equal with the technical.
The class includes basic tutorials for work in Maya leading toward more advanced applications and extensions of the software such as motion capture and algorithms for animating cloth, hair, particles, and grouping behaviors. The first class will meet in CFA room This includes both runtime systems and ; e. In the first part of the course, students will implement small games that focus on specific runtime systems, along with appropriate asset editors or exporters.
In the second part, students will work in groups to build a larger, polished, open-ended game project. Students who have completed the course will have the skills required to extend and ; or build from scratch and ; a modern computer game.
If you meet these requirements but have not taken the formal prerequisite , please contact the instructor. Prerequisite: Physics-Based Rendering Intermittent: 12 units This course is an introduction to physics-based rendering at the advanced undergraduate and introductory graduate level.
During the course, we will cover fundamentals of light transport, including topics such as the rendering and radiative transfer equation, light transport operators, path integral formulations, and approximations such as diffusion and single scattering.
Additionally, we will discuss state-of-the-art models for illumination, surface and volumetric scattering, and sensors. Finally, we will use these theoretical foundations to develop Monte Carlo algorithms and sampling techniques for efficiently simulating physically-accurate images. Towards the end of the course, we will look at advanced topics such as rendering wave optics, and differentiable rendering.
The course has a strong programming component, during which students will develop their own working implementation of a physics-based rendering engine, including support for a variety of rendering algorithms path tracing, bidirectional path tracing, Markov chain Monte Carlo , materials diffuse, glossy, specular, translucent , illumination sources, and sensors. Students will learn common light transport and material models, and be able to write their own code to use these models to create physically-accurate images.
These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision platforms.
The goal of this course is to provide students with the techniques needed for developing complete, integrated AI-based autonomous agents. Topics to be investigated include architectures for intelligent agents, task planning, reasoning under uncertainty, optimization, monitoring, execution, error detection and recovery, collaborative and adversarial multiagent interaction, machine learning, ethical behavior, and explanation.
The course is project-oriented where, over the course of the semester, small teams of students will design, implement, and evaluate autonomous agents operating in a real-world environment. The course's topics include: computational social choice, e.
NOTE: This course is cross-listed with Undergraduates may enroll into but be aware of work load difference. The two courses are identical in terms of lectures, content, and homework assignments. The only difference is in the final project requirement. In , students will prepare a summary of several papers and ; this will require hours of work. In , students will carry out a research project with the goal of obtaining novel results, and present their results in class and ; a good project will require hours of work.
Also note that is 9 units, and is 12 units. Topics include software security, networking and wireless security, and applied cryptography.
Prerequisite: Special Topic: CMRoboBits: AI and Robots for Daily-Life Problems Fall: 12 units This course will be a project-based course in which we will look at AI and robotics artifacts and techniques to automate solutions to real-world problems, in particular related to life in cities.
The course will start by collecting and brainstorming about real problems biased to ones that involve the physical space in addition to the cyber information space, such as traffic rush hour, noise in cities, 3D building inspection, service and data gathering.
We will then formalize the chosen problems and analyze existing real data. The course will proceed by possibly enabling the students to prototype their projects beyond simulation, and using the CORAL lab robots, e.
The course work will be a single large project, performed by groups of up to 3 students. The projects will be divided in three phases, due at the end of February, March, and the end of the course. It covers speech recognition, speech synthesis and spoken dialog systems. The course involves practicals where the student will build working speech recognition systems, build their own synthetic voice and build a complete telephone spoken dialog system.
This work will be based on existing toolkits.
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