Ahmedabad Institute of Technology
Report on
“CoE Workshop-1”
(Statistical Modeling and Predictive Analytics - Machine Learning )
Organized by
Center-of-Excellence , AIT
Ahmedabad Institute of Technology
Nr. Vasant nagar Township, Gota-Ognaj Road,
Ahmedabad
GUJARAT- 382470
The Programme Report
1. Title of the Event Statistical Modeling and Predictive Analytics -
Machine Learning
2. Category Workshop
3. Date 11/09/2023 - 14/09/2023
4. Venue Ahmedabad Institute of Technology, Ahmedabad
5. Total no. of participants 63
6. Expert Name Ms. Samistha Goswami, Mr. Palwinder Singh
7. Event summary The event was aimed at providing a
comprehensive understanding of how to enhance
the workforce being trained in colleges, to match
the skill level demands of the industry is what our
work in this domain aims to do.
8. Mapping with POs PO1, PO2, PO3, PO4,PO6,PO7,
PO9,PO10,PO11,PO12,PSO1, PSO2
Faculty Coordinators:
Dr. Jaimin H. Jani (CE)
Mrs. Snehal Ambulkar (CE)
Ahmedabad Institute of Technology
Objective:
● To introduce Machine Learning.
● To explain how Edunet Foundation is focused on bridging the academia-industry divide,
enhancing student employability, promoting innovation, and creating an entrepreneurial ecosystem
in India. .
● To demonstrate how emerging technologies, and striving to leverage them to augment, and
upgrade the knowledge ecosystem and equip the beneficiaries
● To provide information about Pre-assessment test
Event Overview:
Over 63 students from our 5th semester CE and IT attended the Machine Learning session that
was conducted on 11 September,2024 to 14 September, 2024 by Edunet experts from Code Unnati
at our CE/IT laboratories in block 'D'.
The Session was very fruitful, and now every participant is very eager to continue his or her
upcoming journey with the Center-of-Excellence.
Learning Outcome:
The learning outcomes of a machine learning course typically cover a range of topics and skills
that enable students to understand, implement, and apply machine learning algorithms effectively.
Here are some common learning outcomes:
1. Understanding of Machine Learning Concepts: Students have grasped the fundamental
concepts of machine learning, including supervised learning, unsupervised learning, reinforcement
learning, and semi-supervised learning.
2. Algorithms and Techniques: Students learned about various machine learning algorithms and
techniques such as linear regression, logistic regression, decision trees, support vector machines,
k-nearest neighbors, neural networks, clustering algorithms, dimensionality reduction techniques,
and ensemble methods.
3. Evaluation and Validation: Students understood how to evaluate and validate machine
learning models using appropriate metrics and techniques like cross-validation, ROC curves,
precision-recall curves, and confusion matrices.
4. Feature Engineering: Ability to preprocess and engineer features to improve the performance
of machine learning models, including techniques like feature scaling, one-hot encoding, feature
selection, and handling missing data.
5. Model Selection and Tuning: Understanding the process of selecting the best model for a
given problem and tuning its hyperparameters to achieve optimal performance, often through
techniques like grid search or randomized search.
6. Model Deployment: Knowing how to deploy machine learning models into production
environments, including considerations for scalability, latency, and model monitoring.
7. Ethical and Social Implications: Awareness of the ethical and social implications of machine
learning, including issues related to bias, fairness, transparency, and privacy.
8. Hands-on Experience: They experienced the practical implementing machine learning
algorithms using programming languages such as Python, R, or MATLAB, and utilizing libraries
like scikit-learn, TensorFlow, or PyTorch.
9. Real-world Applications: Students understood the application of machine learning techniques
across various domains such as healthcare, finance, marketing, computer vision, natural language
processing, and recommendation systems.
10. Critical Thinking and Problem-Solving: Students developed critical thinking skills to
analyze data, formulate machine learning problems, and devise appropriate solutions, often
through iterative experimentation and refinement.
By achieving these learning outcomes, students can gain a solid foundation in machine learning
and be better equipped to tackle real-world problems in a variety of domains.
Topics covered during Workshop:
1. Unit 1. Machine Learning Introduction
2. Unit 2.
2.1 Linear Regression
2.2 Logistic Regression
2.3 KNN
2.4 Decision Tree
2.5 Support Vector
2.6 Ensemble Learning
3. Unit 3
3.1 Unsupervised Learning
3.2 K Means Clustering
3.3 Hierarchical Clustering
3.4 Principal Component Analysis.
List of the Participants:
Kindly refer Annexure 1.( Annexure 1
https://drive.google.com/file/d/1RpCcSwZLU2F7HHZZ-rl6_LUXY1emWXZP/view?
usp=drive_link )
Glimpses of the event:
Feedback:
Suggestions by Participants:
● More and more real-world examples should be provided to solidify theoretical knowledge.
● Recommendations should be offered for further reading, online courses, tutorials,
● More resources should be provided to help participants continue learning beyond the workshop.
● More networking sessions or group activities should be conducted to enable participants to
connect with peers, share ideas, and potentially collaborate on future projects.
● More real-world examples and case studies should be given to help participants understand how
machine learning is applied in different industries and contexts.
