This is a course created for conducting Placement Preparation for the students of PUCSD..
There will be programming test, Short/descriptive/MCQ exams towards Placement Activity.
This course will introduce the users how to use MOODLE with various demonstrations.
Various assingments will be added for Using and Learning process of Moodle software.
Users will get badges upon completion of activities. Only teachers from SPPU Campus are supposed to register for this course.
This course will be used only for End Semester Evaluation purpose
This course is used for End Semester Evaluation purpose only.
Students who failed to pass the external examination in Nov-2019 should only register for this course/
** Enrolment for this course is only for External Evaluation.
The course is aimed at providing students with a comprehensive vision of the foundations of concurrent and distributed programming. The main focus of the lectures is on system models and on different types of frameworks intended to support the development of concurrent systems at different abstraction levels and on different underlying platforms. Students will acquire the basic skills to participate in the design, implementation and integration of concurrent and distributed software systems, possibly made of heterogeneous components.
Prerequisites: knowledge of Operating Systems basics and Programming languages like Python and Erlang
** Enrolment for this course is only for EXTERNAL Evaluation.
This course is intended to teach the basics involved in data representation and digital logic
circuits used in the computer system. This includes the general concepts in digital logic design,
including logic elements, and their use in combinational and sequential logic circuit design. This course
will also expose students to the basic architecture of processing, memory and I/O organisation in a
This course will be used only for EXTERNAL EVALUATION purpose...
This course will be used only for EXTERNAL EVALUATION purpose...
Introduction to Machine Learning takes the students through basic Machine learning techniques. Machine learning, as the word implies, refers to how machines can perform tasks without being explicitly programmed to do so. The techniques are widely classified as Supervised Learning, Unsupervised Learning and Reinforcement Learning.
In this course, the focus is on Supervised and Unsupervised Learning techniques, with some pointers on reinforcement learning, if the time permits.
The students having backlog in LLAP course (External examination) should only register for this course.
The subject introduces the students to Systems modelling concepts, continuous and discrete formalisms, Framework for Simulation and Modelling, modelling formalisms and their simulators, discrete time, continuous time, discrete event, process based, Hybrid systems and their simulators.
A Review of basic probability, probability distributions, estimation, testing of hypotheses, selecting input probability distributions, models of arrival processes is done.
Concepts and algorithms for Random number generation, their evaluation, generating random variates from various distributions forms a core part of the subject.
Output analysis, transient behaviour, steady state behaviour of stochastic systems, computing alternative systems, variance reduction techniques.
The course covers nature of O.R., History, Meaning, Models, Principles Problem solving with mathematical
models, optimization and the OR process, descriptive vs. Simulation, exact vs. heuristic
techniques, deterministic vs. stochastic models. Linear Programming, Introduction, Graphical Solution and Formulation of L.P. Models, Simplex
Method (Theory and Computational aspects), Revised Simplex, Duality Theory and applications
Dual Simplex method, Sensitivity analysis in L.P., Parametric Programming, Transportation,
assignment and least cost transportation, interior point methods: scaling techniques, log barrier
methods, dual and primal dual extensions. Introduction to game theory and Multi objective optimization and goal programming
This course covers the idea of constructing of programs by effective use of data organization while building on ideas picked up in the ﬁrst course in this sequence. The course is wovenwith the idea of the dual worlds: algebraic and algorithmic, which leads to a smooth and powerful way to develop programs.
The emphasis of this course will be on algorithm design and analysis. The analysis of algorithms is the process of finding the computational complexity of algorithms – the amount of time, storage, or other resources needed to execute the algorithms using asymptotic notations. By the end of this course the students shall master the art of programming. This course provides perspective on various approaches to studying algorithms,
This is an elective project based course that will provide students with a comprehensive introduction to the theoretical and practical aspects of blockchain technology. It will enable them to develop a deep understanding of inner workings of blockchain technology and will be able to develop blockchain applications. The students have to develop applications (Dapps) using blockchain technology, including cryptography and smart contracts.
A subset of artificial intelligence (AI), machine learning (ML) is the area of computational science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside of human interaction. Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. If any corrections are identified, the algorithm can incorporate that information to improve its future decision making. This elective course will help students to model data using supervised, unsupervised and reinforcement learning algorithms.
Technical Project Delivery Management
- Introduction to Technical Project Management
- What is a Project ? How do projects differ from Operations ?
- Why is Project Management such an important discipline ?
- What are the skills required for selection as Project manager
- Integration of other 8 Knowledge areas and Sub-Processes of Project Management
- Scope Management
- Schedule (Time) Management
- Cost (Financial) Management
- Quality Management (Quality Planning, Quality Assurance and Quality Control)
- Resource Management (Human and non-Human)
- Procurement & Vendor Management
- Communication Management
- Stakeholder Management
- Risk Management
- Advanced topics, Agile Project Management, PMO, Disciplined Agile concepts, Theory of Constraints
The content covered in this coursewould be more focused on cloud compliant and cloud native
solutions. In this agile world, main idea which this course intends is to utilize this opportunity to
help interested students get ready to utilize
cloud based software systems for development and in turn help them build
Theory of Computation is one
of the most important courses in the curriculum at PUCSD. This course addresses
all concepts of automata, formal languages, grammar,
algorithms, computability, and decidability. Always one may ask a
question Why study theory when the current focus of Computer Science is on
technology. Major parts of this course have some amount of impact on practice e.g.
Automata on, compiler design, and search algorithms; Formal Languages and
Grammars on compiler design; and Complexity on cryptography and optimization
problems in manufacturing, business, and management.