P Team 1 , PM B. List the advantage of using Waterfall model instead of adhoc build and fix model. How does "Project Risk" factor affect the spiral model of software development? List out the requirements engineering. What are the linkages between data flow and ER diagrams?

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Problem solving and algorithms. Input and output operations, decision control structure, loop control structure, arrays, strings, etc. Pointers, arrays, structures, functions, file operations, classes, object oriented programming. Lab is also included in this course. Illustration of Proof Techniques in various mathematical topics.

Introduction to Graphs. More Data Types. The objective of this course is to teach fundamentals of Computer Architecture to CSE undergraduate students. Alphabets, languages, finite state machines - deterministic and non-deterministic finite automata. Time and Space bounded computation. Reductions, theory of NP completeness, Introduction to time and space complexity. Functional programming, Object Oriented programming, Logic programming, Lambda calculus, Concurrency, Scripting languages, Programming language semantics.

Syntax directed translators, Finite automata, Regular Expressions, Lexical analysis, Context free languages and grammars, Syntactic analysis, Bottom-up and Top-down Parsing, Syntax directed translation, Lex and yacc as tools for lexical analysis and parsing. Review of compilation process, semantic analysis, intermediate code generation, runtime, code generation, introduction to simple machine independent optimizations. Process management- process states, process vs thread, scheduling algorithms, system calls, IPC.

Programming assignments related to OS features and their implementation. Tanenbaurm as part of the group projects. Introduction to Software Engineering- Importance, challenges, approaches.

Software Processes. Basics of pattern recognition, Bayesian decision theory, Classifiers, Discriminant functions, Decision surfaces, Parameter estimation methods, Hidden Markov models, dimension reduction methods, Fisher discriminant analysis, Principal component analysis, Non-parametric techniques for density estimation, non-metric methods for pattern classification, unsupervised learning, algorithms for clustering- K-means, Hierarchical and other methods.

Parallel and distributed database systems. Adaptive query processing. Streaming databases. Data warehousing and OLAP. Spatial databases and indexing of spatial data. NP-hardness and approximation, approximation ratios and schemes, greedy algorithms, set cover, linear programming and rounding, primal-dual method, FPTAS for knapsack problem, bin packing, Euclidean TSP, introduction to hardness of approximation.

Introduction to Representation, Learning, Detection, Recognition of objects, activities and their interactions from images and videos; Human visual recognition system; Recognition methods- Low-level modeling e. The objective of this course is to learn basic and advanced compiler optimization techniques, either traditional or modern in their scope, or scalar-variable based or loop-optimization based in their application or machine independent or dependent in their variety.

The initial part of the course would be devoted to a collection of traditional compiler analyses and optimizations that are primarily based on control flow and data flow analyses. This will be followed by studying more high-level optimizations that are based on the static single assignment intermediate representation as well as low-level optimizations like register allocation and instruction scheduling and software pipelining.

The later part of this course would be devoted to a model named polyhedral compilation where for-loops can be transformed to run efficiently on advanced architectures like multi-core or GPU using rational and integer linear programming techniques.

Here, the focus would be on basics of the three phase process of dependence analysis, affine scheduling and code generation. Software-Defined Networks is an active research topic to address the existing issues in the enterprise and global networks as well as to enable innovative networking that is not restricted by the traditional network architecture.

This course conducts the analysis and solution development for the existing challenges in the computer networks. We introduce SDN for the solution development, system design and its implementation. The expected outcome of this course is the running source codes and systems that will be proposed and developed by the students as well as a writing for publishing such outcomes for public.

The course introduces indexing techniques for spatial and temporal data, covering even more abstract metric spaces. It describes a range of indexing techniques targeting different types of data, including their underlying principles and properties, as well as their support for queries and updates.

The contents of this course are collected from state-of-the-art research papers i. IntroductionThe parallel R taxonomyLappy and foreach-based parallelismMap reduce based parallelism. This course will provide an introduction to parallel and concurrent programming. It will focus both on correctness and efficiency of multi-threaded programs.

Probability Theory- Probability space, Random variables, probability distributions, joint and conditional distributions. Information Theory- Entropy, mutual information, divergences, Hypothesis testing.

N-gram and continuous space language models, distributed representations, probabilistic taggers and sequence labeling HMM, maximum entropy models, conditional random fields , probabilistic parsing and structured prediction, probabilistic topic models, statistical machine translation. It covers foundations of cryptography, system security, network security, Wi-Fi security, web security, mobile platform security with hands-on assignments and projects.

Introduction to video content analysis, feature extraction, video structure analysis —shot and scene segmentation, content based video classification, video abstraction — skimming and summarization, event detection and classification, indexing for retrieval and browsing, Applications —Movie and sports video analysis, news video indexing and retrieval etc.

Understand the fundamental principles of access control models and techniques, authentication and secure system design.

Have a strong understanding of different cryptographic protocols and techniques and be able to use them. Apply methods for authentication, access control, intrusion detection and prevention. Introduction Motivating examples, Basic concepts- confidentiality, integrity, availability, security policies, security mechanisms, assurance.

Reading research papers in the area of cryptology and understanding the state of the art in the subject. This course will involve a reading of important papers in the area of formal methods. It will be preceded by a review of prerequisite concepts in logic, verification, model checking and automata theory. Compressive Sensing and Dictionary Learning- Pursuit algorithms and applications for imaging and vision.

This course aims for students to 1 understand and apply fundamental mathematical and computational techniques in computer vision and 2 implement basic computer vision applications. Students successfully completing this course will be able to apply a variety of computer techniques for the design of efficient algorithms for real-world applications, such as optical character recognition, face detection and recognition, motion estimation, human tracking, and gesture recognition.

The topics covered include image filters, edge detection, feature extraction, object detection, object recognition, tracking, gesture recognition, image formation and camera models, and stereo vision.

This advanced graduate level course will focus on a melange of selected topics in Compiler Optimizations.

It is mostly a research based course where the registrants will focus on studying state-of-the-art algorithms, in a traditional setting or in the polyhedral compilation- studying and improving the existing algorithms published in top compiler conferences or the ones implemented in LLVM, Polly, PPCG, Pluto, etc.

This advanced graduate level course on combinatorics will focus on selected topics such as extremal combinatorics, probabilistic techniques, algebraic method in combinatorics etc. This advanced graduate level course on machine learning will focus on selected topics such as deep learning, probabilistic graphical models, optimization in machine learning, etc. The course assumes that the student has basic knowledge in machine learning, and will have a research focus.

The objective of the course will be to get a deeper understanding of machine learning algorithms, especially those that are highly relevant for contemporary real-world applications. Storing, indexing and querying document dataScoring, term weighting document relevance estimationText classification and clusteringProbabilistic information retrievalRanking in a Graph. All the above makes engineering modern real-world compilers also a hard software-engineering problem.

This 1 credit course will focus on understanding these issues, taking the popular LLVM compiler as a case-study. Adding new passes. Many real world problems reduce to solving a set of constraints. From time table scheduling to inventory management and fault localization to efficient resource utilization, it all ultimately boils down to expressing these problems as a set of constraints. Not only it is at the heart of most of the problems in operation research but constraint solving has applications ranging from computational biology to program analysis.

These applications use the constraint solvers mostly as a black box. However, one can gain tremendously from the study of constraint solvers and the techniques they employ so as to adapt them to the problem at hand.

This course will attempt to study the underlying techniques employed by modern day constraint solvers. Course Outline- Software has penetrated almost every aspect of our lives.

From banking applications to air traffic control, from pacemakers to smart cars uses some software component. It is therefore of paramount importance that these software work correctly. In this course, we will study various ways to formally analyze and reason about software systems. The course may cover topics such as Hoare logic, abstract interpretation, abstraction refinement, k-induction, symbolic execution, variants of bounded model checking for sequential as well as concurrent programs such as loop bounding, context bounding and reorder bounding.

Use of formal techniques for software testing and reasoning about termination can also be covered. Course Outline- Bayesian data analysis fits a probability distribution over the data and summarize the results by a probability distribution on the parameters of the model and on unobserved quantities.

Bayesian models allow the incorporation of prior information and domain knowledge which helps to better model the data and observations.

This is especially useful for applications such as healthcare and computational biology with limited data availability. The course will cover various topics on bayesian data analysis such as single and multi-parameter models, regression models, hierarchical models, generalized linear models, spatio-temporal models, bayesian decision theory, Model selection, Bayesian inference algorithms based on Monte Carlo methods, variational inference, quadrature and expectation propagation, Bayesian non-parametric approaches such as Gaussian processes and Dirichlet processes, Point processes, Bayesian optimization and Bayesian deep learning.

Optional topics- Integer factoring, lattices. The students are also expected to review and critique one recent research paper during the course. This course will discuss advanced topics and current research in computer vision. Students are expected to read papers selected from various subareas such as deep learning, segmentation and grouping, object and activity recognition, scene understanding, and vision and language.

Approaches for learning from image and video data will be covered and include topics from convolutional neural networks, recurrent neural networks, structured predictions and others. The course will be a mix of lecture, student presentation and discussion. This course will introduce students into the complex, abstract world of computer vision and deep neural networks.

Topics covered will include- Basics of deep learning and its history, State-of-the-art deep neural net models in computer vision; Specific tools and packages to train these deep nets; and what it takes to train and run these models in the real-world. Matrices, Vectors and Properties; Vector Spaces, Norms, Basis, Orthogonality; Matrix Decompositions- Eigen decomposition, Singular Value Decomposition; Differential Calculus- Derivatives and its significance, Partial derivatives; Optimization of single variable and multiple variable functions- Necessary and sufficient conditions; Real problems as optimization problems- Formulation and analytical solutions; Finding roots of an equation- Newton Raphson Method; Optimization via gradient methods; Probability basics, density function, counting, expectation, variance, independence, conditional probability, Poisson process, recurrences, Markov chains.

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