UCS757: Building Innovative Systems | 2022 (July - December)

  • Marking Scheme
  • Tentative Schedule*:
    • Before MST (3 Projects, 2 MCQ)
    • After MST (1 Projects, 1 MCQ, Hack@Kaggle)
  • Learning Resources and Sample Research Papers | Click Here
Projects (Four) | 40 Marks
 
General Instruction:
  • Multiple submissions are allowed, but the latest submission will be considered for evaluation.

  • The submission link will open all the time, but only 50% of marks will be awarded if you fail to submit within the due date. No excuse will be considered for the submission.

  • -10 marks will be awarded for plagiarized code or result.

Projects:
  • 01 - Prediction in time series dataset
  • 02 - Feature selection in high dimension dataset
    • Due Date: 15 Oct 2022 | 08:00
  • 03 - Parameter Optimization of Machine Learning Models
    • Due Date: 15 Oct 2022 | 08:00
  • 04 - Performance improvement of Machine Learning Model using Data Discretization
    • Due Date: 15 Oct 2022 | 08:00

 
MCQ (Three) | 45 Marks | LMS
General Instruction:
  • 8 Questions | 8 Minutes | 15 Marks

  • Question Type: MCQ / True-False / Fill in the blanks

  • Platform: LMS

  • Navigation: Off

  • Exam Link: available on LMS

  • No Makeup

  • Venue: LT101/102

  • Physical presence is mandatory

  • Bring your own laptop, etc.

  • Note:

    • -10 marks will be awarded if the exam is attempted without physical attendance.

    • Special permission may be granted in case of emergency.

MCQ-1 | 15 Marks | Date: 01-09-2022 | Time: 5:15 pm
Topics:
  • Basics of Neural Network

  • Gradient Descendent

  • Regression

  • Study material | Click Here

  • You can refer to any other available resources also.

MCQ-2 | 15 Marks | Date: 08-09-2022 | Time: 5:15 pm
Topics:
  • Basics and advances of NLP, Bag of Word,

  • Python library for NLP, Web Scrapping

  • Study material | Click Here

  • You can refer to any other available resources.

MCQ-3 | 15 Marks | Date: NA | Time: 5:15 pm
Topics:
  • Basics of  - Image processing, CNN, RNN, LSTM, GAN.

  • Study material | Click Here

  • You can refer to any other available resources.

Hack@Kaggle | 15 Marks
 

Rules:

  • Don't cheat!

  • Try yourself!

  • Have fun!

Please Note:

  1. Dates:

    • Start Date: NA | 5:00 pm

    • End Date: NA | 8:00 am

  2. Problem type: Binary Classification

  3. Multiple submissions allowed: Yes (20 submissions/day)

  4. Please try this hack without any external help, if found copied then -10 will be awarded to your total marks.

  5. No makeup

  6. Marking Scheme: Based on the ranking on the leaderboard of Kaggle

    • Top 10% = 15 marks

    • Next 10% = 12 marks

    • Next 10% =  9 marks

    • Next 10% =  6 marks

    • Next 10% =  3 marks

    • Remaining = 1 Marks

    • Absent/No submission: 0 Marks

    • Copied/Plag = -10 will be awarded to your total marks.

  7. The Hack link and other information will be available on LMS/psrana.com on or before the start of the hack.

  8. Train and Test files will be available on Kaggle.

  9. For any other issues email me (prashant.singh@thapar.edu).

 
Learning Resources and Sample Research Papers
Learning Resources
  • Computer Vision (Code, Dataset and Research Papers) | Click Here
  • Natural Language Processing (Code, Dataset and Research Papers) | Click Here
  • Research Papers on Al, NLP, Finance, Time Series, CV, Cryptography, Security, etc. | Click Here

Sample Research Papers
  • 01 - Automated Audio Genre Classification using Ensembled Techniques | Click Here
  • 02 - Quality assessment of modeled protein structure using physicochemical properties | Click Here
  • 03 - Multilevel ensemble model for prediction of IgA and IgG antibodies | Click Here

  • 04 - Fraudulent Firm Classification A Case Study of an External Audit | Click Here

  • 05 - Ensemble Technique for Prediction of T‑cell Mycobacterium | Click Here

  • 06 - Classification of drug molecules for oxidativestress signalling pathway | Click Here

UCS757: Building Innovative Systems | 2021 (July - December)

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4. Submission Link: Click Here

  • Multiple submissions are allowed, but the latest submission will be considered for the evaluation.

  • Kindly submit before the due date.

 
 
Learning Resources
  • Computer Vision (Code, Dataset and Research Papers) | Click Here
  • Natural Language Processing (Code, Dataset and Research Papers) | Click Here
  • Research Papers on Al, NLP, Finance, Time Series, CV, Cryptography, Security, etc. | Click Here

 
Sample Research Papers
  • 01 - Automated Audio Genre Classification using Ensembled Techniques | Click Here
  • 02 - Quality assessment of modeled protein structure using physicochemical properties | Click Here
  • 03 - Multilevel ensemble model for prediction of IgA and IgG antibodies | Click Here

  • 04 - Fraudulent Firm Classification A Case Study of an External Audit | Click Here

  • 05 - Ensemble Technique for Prediction of T‑cell Mycobacterium | Click Here

  • 06 - Classification of drug molecules for oxidativestress signalling pathway | Click Here

 
MCQ-1 | 15 Marks | 10-09-2021 | 6:15 pm
General Instruction:
  • 8 Question | 8 Minutes | 15 Marks

  • Date: 10-09-2021

  • Time: 6:15 pm

  • No Makeup

  • For slide | Click Here

  • You can refer to any other available resources.

  • Exam link will be available on LMS

Topics:

  • Basics of Neural Network

  • Gradient Descendent

  • Regression.

 
MCQ-2 | 15 Marks | 30-09-2021 | 6:15 pm

General Instruction:

  • Date: 30-09-2021

  • Start Time: 6:15 pm

  • No of Questions: 8

  • Time limit: 8 mins

  • Total marks: 15

  • No Makeup

  • Exam link will be available on LMS

Topics:

  • Basics and advance of NLP

  • Bag of Word,

  • Python library for NLP,

  • Web Scrapping

  • Study material | Click Here

  • You can refer to any other available resources.

 
MCQ-3 | 15 Marks | 27-11-2021 | 6:15 pm

General Instruction:

  • Date: 27-11-2021

  • Start Time: 6:15 pm

  • No of Questions: 8

  • Time limit: 8 mins

  • Total marks: 15

  • No Makeup

  • Exam link will be available on LMS

Topics:

  • Basics of  - Image processing, CNN, RNN, LSTM, GAN.

  • Study material | Click Here

  • You can refer to any other available resources.