Time - Plan for NWMLDS - 2020

Time 30th July 31st July 1st August 2nd August 3rd August

Keynote 2: Democratizing AI in Developing Countries (SM)

Short Break
Talk 6: Applications of Deep Learning on multi-scale physics based simulators (JD)

Short Break
Talk 10: Deep Learning for video classification: Application and state of the art approach (SRS)

Short Break


Networking + project discussion (for participants)

Keynote 3 : Robust Data Mining for not to be misled by Numbers (SA)

Short Break
Networking + project discussion (for participants)

Networking + project discussion (for participants)

11:00 -12:15

12:15 - 12:30
Keynote 1: Introduction to Machine Learning (SRJ)

Short Break
Talk 3: Artificial intelligence in Education 4.0 during/after Pandemic (MP)

Short Break
Talk 7: Shallow and deep learners for tabular dataset (RK)

Short Break
Poster presentation

Panel Discussion

12:30 -13:00

13:00 - 13:45
Sponsor Time

Lunch Break
Sponsor Time

Lunch Break
Sponsor Time

Lunch Break
Sponsor Time

Lunch Break
Sponsor Time

Lunch Break
13:45 - 15:00

15:00 - 15:15
Talk 1: Supervised Machine Learning pipeline: Step by step Tutorial (TBS)

Short Break
Talk 4: Effective Approaches and Machine Learning Algorithms to deal with time series data (JP)

Short Break
Talk 8: Machine Learning approach to solving natural language processing problems (BKB)

Short Break
Talk 11: Generative Adversarial Network (BB)

Short Break
Talk 13: Methods used for the development of deep learning for remote sensing applications (BM)

Short Break

Talk 2: Application of Machine Vision and Deep learning in Agriculture (AK)

Talk 5: On managing data science artifacts (RS)

Talk 9: Practical Reinforcement Learning (AP)

Talk 12: Big Data Processing with Pyspark (BG)

Talk 14: Causal Modelling (DR)


Project session (for participants)

Project session (for participants)

Project session (for participants)

Project session (for participants)

Closing Session

Workshop Topics

  • Machine Learning

    • Introduction
    • Basics of python (installation, execution, Packages)
    • Supervised Learning
    • Unsupervised Learning
    • Recent Advances in ML
  • Deep Learning

    • Convolution Neural Network
    • Transfer Learning
    • Recurrent Neural Network
    • Application of DL
  • Data Science

    • Data acquisition and Cleaning
    • Data Visualiation
    • Data Exploration
    • Data Analysis
    • Spatial data analysis
    • Big Data Analysis
    • Unstructured data analysis
  • Natural Language Processing

    • Basic of NLP and Computational Linguistics
    • Natural Language Understanding
    • Natural Language Generation

Panel Discussion: Scope of ML and DS in Nepal

List of Projects

Project 2: "Sentiment Analysis : An Analysis of Deep Learning Models":

In this project the idea is to implement 3 Sentiment Analysis models using LSTM, GRU and also CNN and compare accuracies.

Data source:

Project 3: Face detection for masks

We create a deep learning system using OpenCV and Keras/PyTorch/Tensorflow to determine if masks are worn or not.

Reference article:

Project 4: Detect pneumonia using X-ray images
Project 5: Beginners project with Titanic dataset from Kaggle (for novice)
Project 6: Beginners project with hand-written character recognition (for novice)
Project 7: Stock price forecasting
Project 8: A project on Anomaly detection, dataset from Kaggle