Free download. Book file PDF easily for everyone and every device. You can download and read online Subspace Learning of Neural Networks (Automation and Control Engineering) file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Subspace Learning of Neural Networks (Automation and Control Engineering) book. Happy reading Subspace Learning of Neural Networks (Automation and Control Engineering) Bookeveryone. Download file Free Book PDF Subspace Learning of Neural Networks (Automation and Control Engineering) at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF Subspace Learning of Neural Networks (Automation and Control Engineering) Pocket Guide.

Data compression and Kraft's inequality, source coding theorem and Shannon entropy, Kullback-Leibler divergence and maximum entropy, I-projections and Sanov theorem, Kullback- siszar iteration and iterative scaling algorithms, Fisher information and Cramer-Rao inequality, quantization and introduction to rate distortion theory, generalized information measures and power-law distributions.

Control Systems, Robotics, and Automation

References: Elements of Information Theory, by T. Cover and J. MacKay, Cambridge University Press, The use of randomness in algorithm design is an extremely powerful paradigm. Often, it makes algorithm design and analysis easier; however there are some problems for which we only know randomized algorithms and no deterministic algorithms.

Furthermore, depending on the model of computation, randomization is often essential -- it provably does better than all deterministic algorithms. In this course, we will introduce the basic techniques of designing randomized algorithms although at times we will dive into state-of-the-art topics. Students are expected to have taken an introductory course in algorithm design and analysis, and some familiarity with probability, although not essential, is desirable. Elementary number theory, Finite fields, Arithmetic and algebraic algorithms, Secret key and public key cryptography, Pseudo random bit generators, Block and stream ciphers, Hash functions and message digests, Public key encryption, Probabilistic encryption, Authentication, Digital signatures, Zero knowledge interactive protocols, Elliptic curve cryptosystems, Formal verification, Cryptanalysis, Hard problems.

References: Stinson. Cryptography: Theory and Practice. Handbook of Applied Cryptography. Information retrieval using the Boolean model. The dictionary and postings lists. Tolerant retrieval. Index construction and compression. Vector space model and term weighting. Evaluation in information retrieval. Relevance feedback and query expansion. Probabilistic information retrieval. Language models for information retrieval. Text classification and clustering. Latent semantic indexing. Web search basics. Web crawling and indexes. Link analysis. References: C. Manning, P. Raghavan, and H. Recent Literature.

Concepts of Agency and Intelligent Agents. Action of Agents, Percepts to Actions. Applications: Web-based Agents, Database Applications. Agent Programming. References: S. Russel and P. Recent Papers. Introduction to Artificial Intelligence, Problem solving, knowledge and reasoning, Logic, Inference, Knowledge based systems, reasoning with uncertain information, Planning and making decisions, Learning, Distributed AI, Communication, Web based agents, Negotiating agents, Artificial Intelligence Applications and Programming. George F. Luger, Artificial Intelligence, Pearson Education, Nils J.

Models of concurrency: multi-threading, synchronization, event-based dispatch. Model checking: model checking abstractions, context bounding, partial order reduction. Static analysis: type systems for proving dealock and race freedom, rely guarantee framework for compositional reasoning. Vulnerability detection: overflow, heap, and string analyses; information flow.

References: M. Introduction to Probability theory, Random variables, commonly used continuous and discrete distributions. Introduction to Stochastic Process, Poisson process, Markov chains, steady stateand transient analysis.

Featured channels

Psuedo random numbers: Methods of Generation and testing. Methods for generating continuous and discrete distributions. Methods for generating Poisson Process. Building blocks of Simulation, Data Structures and Algorithms. Introduction to Probabilistic modelling, Maximum Likelihood Variance reduction techniques: antithetic variates, control variates, common random numbers, importance sampling.

Analysis of Simulation results: confidence intervals, design of experiments.


Markov Chain Monte Carlo techniques. References: Sheldon M. Law and W. References: I. Walrand and P. Bertsekas and R. Kurose and K. Bishop - Pattern Recognition J. Manning and H. Processor Architecture: Instruction-Level Parallelism,Superscalar and VLIW architecture; Multi-core processors;Memory Subsystem: Multilevel caches, Caches in multi-core processors,Memory controllers for multi-core systems;Multiple processor systems: shared and distributed memory system,memory consistency models, cache coherence, and Interconnection networks;Advanced topics in architecture.

References: Hennessy, J. Stone, H. Current literature. Prerequisites E : Design and Analysis of Algorithms. This course is a complexity-theoretic introduction to Cryptography. Emphasis will be placed on exploring connections between various fundamental cryptographic primitives via reductions. Some of the primitives we will cover are one-way functions, pseudo-random generators, pseudo-random functions, trapdoor permutations, encryption, digital signatures, hash functions, commitments.

We will also try to cover some special topics private information retrieval, zero-knowledge proofs, oblivious transfer etc. Combinatorial algorithms: greedy algorithms, local search based algorithms; Linear programming based algorithms: randomized rounding, primal-dual schema based algorithms, iterated rounding; multicut, sparsest cut and metric embeddings; Semidefinite programming based algorithms; Hardness of approximation. Prerequisites E Design and Analysis of Algorithms. Abstract data types and data structures, Classes and objects, Complexity of algorithms: worst case, average case, and amoritized complexity.

Algorithm analysis. Algorithm Design Paradigms. Lists: stacks, queues, implementation, garbage collection. Priority queues. Graphs: Shortest path algorithms, minimal spanning tree algorithms, depth-first and breadth-first search. Sorting: Advanced sorting methods and their analysis, lower bound on complexity, order statistics.

References: A. Aho, J. Hopcroft, and J. Cormen, C. Leiserson, and R. Example languages from each of the above categories would be discussed along with their implementation details. Formal semantics would be used to enhance the understanding of the features and to assist in the design of correct implementations.

TCLab A: Arduino + Deep Learning

However, there will be no deep discussion of the theory. This is neither a course on compiler design nor a course on the theory of programming languages. Emphasis would be on understanding the features and their implementation. Students will be required to carry out mini projects as a part of the course.

Features and implementation of imperative, object-oriented, concurrent, distributed, logic-programming, functional, aspect-oriented, scripting, business-oriented and web programming languages. Selected papers. There will be no overlap with the compiler design course in the CSA department E0 User Level Specification of OS.

Management and Control of Processes. Traditional and Real-Time Signals. Clocks, Timers and Callouts. Protection and Security. References: Andrew S. Mauro and R. Daniel P. Current Literature. Prerequisites Knowledge of Java is desirable, but not necessary. Control flow graphs and analysis; Dataflow analysis; Static single assignment SSA ; Compiler optimizations; Dependence analysis, Loop optimizations and transformations, Parallelization, Optimizations for cache locality, and Vectorization; Domain-specific languages, compilation, and optimization; Register allocation, Instruction scheduling; Run time environment and storage management; Impact of language design and architecture evolution on compilers.

References: Aho, A. This course will seek to equip students with the fundamental principles and practice of computer systems security. The course will cover the major techniques of offense and defense, thereby educating students to think both as attackers and defenders. By the end of the course, students will have been exposed to the state of the art, and will be equipped with the background to start conducting original research in computer systems security.

Core concepts such as basic security goals, threat models, notion of TCB and security policies vs. Operating system primitives for protection, reference monitors, authentication, and authorization. Examples of classic security policies from the literature e. Various forms of hijacking attacks, such as buffer overflows, return-oriented programming, and non-control data attacks, and examples of such attacks as used by exploits in the wild. Design and implementation of defenses such as control-flow integrity, ASLR, privilege separation, capabilities, information-flow control and virtual machine introspection.

Attacks and defenses against the Web ecosystem, mobile devices and the cloud platform. Emerging role of modern hardware in improving systems security. Other assorted topics based on current research literature. Prerequisites None, but standard undergraduate-level exposure to OS, computer architecture and compilers courses will be assumed.

Software process and the role of modeling and analysis, software architecture, and software design. Software Architecture: architectural styles, architectural patterns, analysis of architectures, formal descriptions of software architectures, architectural description languages and tools, scalability and interoperability issues, web application architectures, case studies. Software Design: design best practices, design patterns, extreme programming, design case studies, component technology, object oriented frameworks, distributed objects, object request brokers, case studies.

References: Booch,G. Gamma, E. Johnson, R. Vissides, J. Frank Buschmann et al. John Wiley and Sons, Shaw, M. Len Bass et al. Software Architecture in Practice. Survey of programming paradigms and computational models for program execution. Programming language examples, syntax description and language semantics Functional programming, lamda calculus, Higher-order functions, currying, recursion.

Imperative programming and control structures, invariants, object models, messages, and method dispatch, inheritance, subtypes and subclasses, polymorphism, covariance, and contravariance. Formal aspects of Java. Concurrent programming models and constructs, programming in the multi-core environment. Introduction to Logic programming quantifiers, first order logic, Horn clauses, unification and resolution.

  1. Crowned ( La Femme Fatale Publishing)?
  2. Browse more videos;
  3. U.S. Periods of War and Dates of Current Conflicts.
  4. The Nourish Series: Baby Nutrition.
  5. Dons Airbrush Tips!

Press, Selected Chapters from J. Current research papers and Internet resources. Data Analytics is assuming increasing importance in recent times. Several industries are now built around the use of data for decision making. Several research areas too, genomics and neuroscience being notable examples, are increasingly focused on large-scale data generation rather than small-scale experimentation to generate initial hypotheses.

This brings about a need for data analytics. This course will develop modern statistical tools and modelling techniques through hands-on data analysis in a variety of application domains. The course will illustrate the principles of hands-on data analytics through several case studies such studies. On each topic, we will introduce a scientific question and discuss why it should be addressed. Next, we will present the available data, how it was collected, etc.

We will then discuss models, provide analyses, and finally touch upon how to address the scientific question using the analyses. Data sets from astronomy, genomics, visual neuroscience, sports, speech recognition, computational linguistics and social networks will be analysed in this course. Statistical tools and modeling techniques will be introduced as needed to analyse the data and eventually address these scientific questions.

There will be a few guest lectures from industry also. References: Database Systems Concepts, H. Korth, A. Silberschatz and S. Elmasri and S. Navathe, Addison-Wesley. Database Management Systems R. Ramakrishnan and J. Gehrke, McGraw-Hill. Readings in Database Systems M. Stonebraker and J. Hellerstein, Morgan Kaufmann. Recent Conference and Journal papers. Kenneth P. Coulouris, J. Dollimore, and T. To introduce the student to the soft computing paradigm as compared to hard computing. To make them learn the techniques of soft computing like neural networks, fuzzy and rough systems, evolutionary algorithms etc.

Definition of soft computing, Soft computing vs.

EUSIPCO 26th European Signal Processing Conference (EUSIPCO) (Roma, Italy) - Tutorials

Hard computing; Advantages of soft computing, tools and techniques;Neural Networks : Fundamentals, backpropogation, associative memory, self organizing feature maps, applications;Fuzzy and rough sets : Concepts and applications; Evolutionary algorithms, swarm intelligence, particle swarm optimization, ant colony optimization, applications;Hybrid systems : Integration of neural networks, fuzzy logic and genetic algorithms, integration of genetic algorithms and particle swarm optimization, Applications. References: Timothy J.

Melanie Mitchell, An introduction to genetic algorithms, Prentice Hall, Prerequisites Linear Algebra, Probability and Statistics, Some programming experience in any language. Graph types : conditional independence; directed, undirected, and actor models; algorithms for conditional independence e. Static Models : linear Gaussian models, mixture models, factor analysis, probabilistic decision trees, Markov Random Fields, Gibbs distributions, static conditional random fields CRFs , multivariate Gaussians as graphical models, Exponential family, generalized linear models, factored exponential families.

Dynamic temporal models : Hidden Markov Models, Kalman filtering and linear-Gaussian HMMs, linear dynamic models, dynamic Bayesian networks DBNs , label and observation bias in natural language processing, dynamic conditional random fields CRFs , and general dynamic graphical models.

An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network

Chordal Graph Theory : moralization; triangulated, decomposable, and intersection graphs, Tree-width and path-width parameters of a graph. Exact Probabilistic Inference : The elimination family of algorithms. Relation to dynamic programming. NP hardness results. Approximate Probabilistic Inference : loopy belief propagation BP , expectation propagation EP , Sampling markov chains, metropolis hastings, gibbs, convergence and implementaional issues particle filtering.

Structure Learning : Chow Liu algorithm. Latent Dirichlet Allocation 1 wk : Exchangeability, de Finetti Theorem, Inference using collapsed Gibbs sampling, Dirichlet compound multinomial model. Introduction to Machine Learning, classification using Bayes rule, introduction to Bayes decision theory. Learning as optimization, linear regression. Hyperplane based classifiers, Perceptron, and Perceptron Convergence Theorem.

Support vector machine and kernel methods. Feedforward neural networks, backpropagation algorithm. Autoencoders, Convolutional neural networks, and application to computer vision. Restricted Boltzmann Machine. References: Bishop. Prerequisites Probability and Statistics or equivalent course elsewhere. Some background in linear algebra and optimization will be helpful. Graphics pipeline; transformations; viewing; lighting and shading; texture mapping; modeling; geometry processing - meshing, multi-resolution methods, geometric data structures; visualization - visualization pipeline, data reconstruction, isosurfaces, volume rendering, flow visualization.

References: Edward S. Angel and Dave Shreiner. Pearson, Addison-Wesley, 8th Edition, Research papers from graphics and visualization conferences and journals. Prerequisites Undergraduate courses in linear algebra, data structures, algorithms, and programming. Domain modeling using first-order predicate logic and relational calculus -- the tools Alloy and Event-B.

Verification of finite-state systems, and concurrent systems -- Spin. Verifying code correctness using logical reasoning -- VCC. Testing and bounded-exploration of applications -- Pex and AFL. Model Checking, by Edmund M. Clarke, Orna Grumberg, and Doron Peled. High-Dimensional Data. Modeling of data in a high dimensional space. High dimensional Euclidean geometry.

Random projection theorem. Random Graphs. Erdos-Renyi model, Properties of random graphs, Giant component and threshold phenomena. Random satisfiability problems. Singular Value Decomposition and its applications. Random Walks: Connections to electrical networks, convergence using eigen values and conductance measures. Foundations of Learning Theory. The perceptron algorithm, margins and support vector machines and Vapnik-Chervonenkis theorem and applications.

Clustering algorithms and criteria. Provable results and algorithms for k-means and other criteria. Recent work on finding local communities using random walks. Massive Data Computations including streaming algorithms. Fast approximations to matrices such as the CUR approximation. Advertisement Hide. Chinese Conference on Biometric Recognition. Conference paper First Online: 24 October This is a preview of subscription content, log in to check access. Belhumeur, P. Zhang, T. Zhong, F. Baudat, G. Zheng, W. Li, J. Jiang, X. Xu, Y.

China ;. Goncalves University of Campinas ; Fernando J.

  1. Subspace Learning of Neural Networks.
  2. Junbin Gao - The University of Sydney Business School.
  3. Flere b√łker av Huajin Tang, Kay Chen Tan og Zhang Yi:.
  4. Junbin Gao.
  5. Hope of Israel!
  6. Browse more videos;

Hauptmann Carnege Mellon University ,. Paper ID: 88 - Schema. Artois - CNRS ;. Lyon 1 ;. Paper ID: - Did you know? Artois ;. Castellanos-Dominguez Universidad Nacional de Colombia ;. Buenos Aires - National Congress. Puerto Madero. The Obelisk, the most popular symbol of Buenos Aires.