1. Workshop on “AI and ML in First Year Labs” – Exploratory Data Analysis

The Department of Artificial Intelligence conducted a hands-on faculty development workshop focused on Exploratory Data Analysis (EDA). The session aimed to equip participants with practical skills in data preprocessing, visualization, and statistical analysis.

The workshop covered essential EDA concepts such as handling missing values, detecting outliers, and transforming data for analysis. Participants learned to create meaningful visualizations using tools like histograms, box plots, scatter plots, and heatmaps, gaining insights into data trends and relationships.

Through hands-on practice and interactive discussions, attendees developed a strong foundation in data analysis techniques, empowering them to incorporate AI and ML concepts more effectively into first-year lab sessions.

2. Two-Day Faculty Development Session on "Integration Of AI And ML In First Year Labs

The Department of Artificial Intelligence conducted a two-day hands-on faculty development workshop focused on the integration of machine learning—specifically regression models—into the mathematics curriculum for first-year students. Designed to bridge theoretical understanding with applied learning, the workshop offered a balanced blend of conceptual instruction and practical implementation.

This workshop empowered faculty to confidently incorporate AI and ML concepts into foundational mathematics courses, helping to enhance student engagement and build a strong analytical mindset from the start of their academic journey.

3. Two-Day Faculty Development Session on "Use Of Regression Machine Learning Model In Maths

The Department of Artificial Intelligence organized a two-day faculty development workshop focused on the application of regression-based machine learning models in mathematical problem-solving. This program provided participants with both theoretical grounding and hands-on experience in implementing regression techniques using real-world data. Faculty members actively engaged in implementing regression models using Python, interpreting performance metrics like R-squared and Mean Squared Error. The interactive nature of the sessions, combined with coding exercises and expert feedback, allowed participants to gain practical insights into integrating machine learning within mathematical contexts. The workshop successfully equipped attendees with the knowledge and skills required to apply regression models to solve real-world mathematical problems, enhancing their instructional and research capabilities.

4. Essentials Of Machine Learning Techniques

The Department of Artificial Intelligence conducted a multi-day Faculty Development Program covering key concepts and practical applications of Machine Learning and Artificial Intelligence. Sessions included Python essentials, EDA, feature engineering, regression (linear and logistic), clustering (k-Means, hierarchical), and classification algorithms such as Decision Trees, Random Forests, SVM, and k-NN.

Participants also explored deep learning basics, research paper writing tools, and engaged in hands-on coding exercises. The program concluded with a live project demonstration and doubt-clearing session, empowering faculty with applied knowledge for effective teaching and research in AI and ML.