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BE Computer Engineering


  1. BE Computer Syllabus
  2. LP1 Programs
  3. LP1 Lab Manual
  4. LP3 Programs
  5. LP3 Writeup
  6. LP4 Writeup
  7. BE GITHUB REPO (P.S. Follow on Github 😁)
  8. BE Notes &  MCQ (Thanks to Rahil Vashistha)

Lab Practice 1 (LP1)

  • High Performance Computing

    1. CUDA Parallel Reduction and Vector Operations :
      1. Implement Parallel Reduction using Min, Max, Sum and Average operations
      2. Write a CUDA program that, given an N-element vector, find
      1. The maximum element in the vector
      2. The minimum element in the vector
      3. The arithmetic mean of the vector
      4. The standard deviation of the values in the vector
    2. Vector and Matrix Operations :
      1. Add two large vector
      2. Multiply two N × N arrays using n 2 processors
    3. Parallel Bubble Sort and Merge Sort :
        1. Bubble Sort & Merge Sort
        2. Github Link
      1.  Parallel Search Algorithms: Binary Search, BFS/DFS
        1. Binary Search
        2. Github Link
    1. Introduction to OpenMP Presentation

  • Artificial Intelligence & Robotics

    1. Solve 8-puzzle problem using A* algorithm. Assume any initial configuration and define goal configuration clearly.
      1. JAVA Implementation
    2. Implement alpha-beta pruning graphically with proper example and justify the pruning.
      1. Python Implementation
      2. Java Implementation
    3. Develop elementary chatbot for suggesting investment as per the customers need.
      1. Java Implementation
    4. Constraint Satisfaction Problem:Implement crypt-arithmetic problem or n-queens or graph coloring problem ( Branch and Bound and Backtracking) 
      1. Python Implementation
      2. Note.: CPP is not allowed for final Practicals in some colleges.
    5. Implement goal stack planning for the following configurations from the blocks world,
      1. Java Implementation
    6. Use Heuristic Search Techniques to Implement Hill-Climbing Algorithm.
      1. Java Implementation
    7. AIR Theory Notes

  • Data Analytics

    1. Download the Iris flower dataset or any other dataset into a DataFrame. (eg https://archive.ics.uci.edu/ml/datasets/Iris ) Use Python/R and Perform  Operations
      1. Github Link
      2. IRIS Flower dataset
    2. Download Pima Indians Diabetes dataset. Use Naive Bayes‟ Algorithm for classification
      1. Python Implementation
      2. Pima Indians Diabetes dataset
    3. Write a Hadoop program that counts the number of occurrences of each word in a text file.
      1. Java Implementation
      2. Hadoop Installation
      3. Word Count Program Explanation
    4. Trip History Analysis: Use trip history dataset that is from a bike sharing service in the United States. The data is provided quarter-wise from 2010 (Q4) onwards. Each file has 7 columns. Predict the class of user. Sample Test data set available here https://www.capitalbikeshare.com/trip-history-data
      1. Python  Implementation
    5. Data Analytics Theory Notes

LP3 Programs & Notes


Mini Projects BE

Other BE Related Content: 

  1. Search By Label: BE
  2. Simple BE LP1 codes by Aditya malte
  3. BE Notes &  MCQ by Rahil Vashistha

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