AI/ML Workshop

 20th Nov & 21st Nov
 2 Days


The sessions are aligned to orient the participants to a basic understanding of Machine Learning and Deep Learning. They will help the participants to get acquainted with generic concepts of data pre-processing, model training and inferencing. The discussion will be aligned to cover case studies in the areas of Computer Vision and NLP with respect to application development. Case studies will also involve different types of data.

Topics of Concern

1. Artificial Intelligence

2. Machine Learning vs Deep Learning

3. Building blocks of a Neural Network

4. Different types of Data used in ML (Image, Audio, Video and Text)

5. Data Pre-processing

6. Intricacies of Model Training

7. Model Optimization and Inferencing

8. Case Studies in CV
a. Image Classification - Satellite Image Classification | Kaggle
b. ObjeAbstract
The sessions
c. ct Detection - Guns Object Detection | Kaggle

9. Case Studies in NLP
a. Text Classification - ATIS Airline Travel Information System | Kaggle
b. Sentiment Prediction - Intro to Deep Learning Sentiment Classification | Kaggle

10. Project: Image/Video Captioning


Participants will be able to apply the basic concepts of Machine Learning and Deep Learning in building applications.

Keynote Speakers

Dr. Priyanka Sharma
Vice Chairman (Technical Activities) IEEE IA/PELS Vice President Projects -Artificial Intelligence Samyak Infotech Pvt. Ltd

Prof. Aakansha Saxsena
Artificial Intelligence Expert IBM Certified Artificial Intelligence Analyst

Workshop details

Theory + Practical: 2 hour

-----------------Date: 20/11/22-----------------

Dr. Priyanka Sharma

◉ Introduction about speakers [5min].
◉ Introduction to Artificial Intelligence Machine Learning vs Deep Learning [15 Min].
◉ Building blocks of a Neural Network [15 Min].
◉ Different types of Data used in ML (Image, Audio, Video and Text) [10 Min].
◉ Real-Time applications of AI ML, DL [15 Min].

Prof. Aakansha Saxena

◉ Real-time applications of ML [5 Min].
◉ Data Preprocessing - Text + Image: [10 Min].
◉ Train the Model - Classification-Text +Image [15 Min].
◉ Regression - Logistics, Linear [10 Min].
◉ Clustering - [10 Min].
◉ Performance Evaluation Metrics-Confusion Matrix, Accuracy, Precision, Recall, F1 Score - [10 Min].

-----------------Date: 21/11/22-----------------

Dr. Priyanka Sharma

Theory: 1 Hour

◉ Advance concepts related to Deep Learning for Text and Image Data [30 min]
◉ Case Study on Image Captioning [30 min]

Prof. Aakansha Saxena

Practical: 1 Hour

◉ Case Study - Sentiment Analysis Description & Hands - On

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