Welcome to the 7th semester syllabus page for the Computer Science and Engineering (CSE) branch at Bihar Engineering University. Here, you'll find an overview of the courses offered during this semester, along with detailed syllabi for selected subjects.
The 7th semester is a crucial phase in your B.Tech journey, as it delves deeper into advanced concepts and prepares you for your final year. The courses offered cover a wide range of topics, from theoretical foundations to practical applications, ensuring a well-rounded education in the field of computer science and engineering.
| Course ID | Course Name | Credits |
|---|---|---|
| 100708 | Biology for Engineers | 3 |
| 100701 | Induction Program | 0 |
| 1057xx | Open Elective- I | 3 |
| 1057xx | Open Elective- II | 3 |
| 1057xx | Program Elective - III | 3 |
| 100709 | Project-I | 6 |
| 100707 | Summer Entrepreneurship - III | 8 |
| 1057xx | Professional Elective Lab-II | 1 |
Logic: First-order predicate calculus - syntax, semantics, validity and satisfiability, decision problems in logic, quantified Boolean formulas and their relation with the polynomial hierarchy.
Computability theory: Review of Turing machines, some other computing models and formalisms, their equivalence with Turing machines, undecidability, Post correspondence problem, Turing computability, primitive recursive functions, Cantor and Goedel numbering, Ackermann function, mu-recursive functions, recursiveness of Ackermann and Turing computable functions, lambda calculus, term rewriting, oracle machines and the arithmetic hierarchy.
Complexity theory: Time- and space-bounded Turing machines, reduction and complete problems, oracle machines and the polynomial hierarchy, randomized computation, parallel computation.
Module 1: Introduction to Data Science: Concept of Data Science, Traits of Big data, Web Scraping, Analysis vs Reporting
Module 2: Introduction to Programming Tools for Data Science, Toolkits using Python: Matplotlib, NumPy, Scikit-learn, NLTK, Visualizing Data: Bar Charts, Line Charts, Scatterplots, Working with data: Reading Files, Scraping the Web, Using APIs (Example: Using the Twitter APIs), Cleaning and Munging, Manipulating Data, Rescaling, Dimensionality Reduction
Module 3: Mathematical Foundations - Linear Algebra: Vectors, Matrices, Statistics: Describing a Single Set of Data, Correlation, Simpson's Paradox, Correlation and Causation, Probability: Dependence and Independence, Conditional Probability, Bayes's Theorem, Random Variables, Continuous Distributions, The Normal Distribution, The Central Limit Theorem, Hypothesis and Inference: Statistical Hypothesis Testing, Confidence Intervals, Phacking, Bayesian Inference