Session

Duration (hours)

SessionAgenda

Assessment

1

3

• Introduction to AI

  • A brief review on AI history

  • AI Applications: State of the art

  • AI, Machine Learning, and Deep Learning

  • Restrictions and constraints

  • Future of AI

• Introduction to Google Colab (Python)

• Introduction to Orange

  • Why Orange?

  • Setting up your system

  • Creating Your First Workflow

• Introduction to Preprocessing of Data Using Python

  • Checking and handling missing values

  • Handling categorical data (Label encoding, One-hot encoding)

  • Standardize/Normalize continuous data

  • PCA transformation

  • Data splitting

Quiz 1

2

3

• Machine Learning Methods Using Orange

  • Importing the data files

  • Understanding our Data

  • Missing Values and Imputation

  • Training your First Model

  • Supervised Learning Algorithms

    • Logistic Regression

    • Random Forest

    • Support Vector Machine

    • K-Nearest Neighbor

    • Neural Network

    • Comparison of Different Models

  • Clustering Algorithms

    • Hierarchical Clustering

    • K-Means

  • Visualization

    • Data Table

    • Scatter Plot

    • Mosaic Display

    • Sieve Diagram

    • Rank

    • Rad Viz

Quiz 2

3

3

• Machine Learning Methods Using Python

  • Clustering and its applications

    • Clustering

      • Definition of Clustering Task

      • Popular Clustering Algorithms: K-means, Hierarchical, DBSCAN

    • Applications

      • Customer segmentation, Image segmentation, Anomaly detection

  • Decision Tree and its applications

    • Decision Tree

      • Definition of Decision Tree task

      • Explain decision tree method

    • Applications

      • Credit risk assessment, Disease diagnosis, Predictive maintenance

Quiz 3

4

3

• Machine Learning Case Studies and Application

  • Using Orange and Python

    • Healthcare: Predicting Disease Outcomes (CLASSIFICATION)

    • Finance: Fraud Detection (OUTLIER DETECTION)

    • Marketing: Customer Segmentation (CLUSTERING)

Quiz 4

5

3

• Machine Learning Case Studies and Application

  • Using Orange and Python

    • Retail: Personalized Recommendations (RECOMMENDER SYSTEMS)
       

• LLM (Large Language Model) Applications

  • Introduction to Large Language Models (LLMs)

    • Brief Overview of LLMs

    • Importance and Applications in NLP

    • Overview of Google's Palm LLM

  • Google Palm LLM in LangChain

    • Introduction to LangChain

    • Integration of Palm LLM in

  • Working with Pretrained Models

    • Importance of Pretrained Models

    • Loading and Using Pretrained Language Models

    • Fine-Tuning Pretrained Models for Specific Tasks

  • Text Generation and Completion with LLMs

    • Autoregressive Generation

    • Sampling Techniques

    • Generating Text with Google's Palm LLM

  • EvaluationandMetricsforLLMs

    • Metrics for Evaluating Language Models

    • Human Evaluation vs. Automated Metrics

    • Assessing the Performance of Google Palm LLM

  • Ethical Considerations in LLMs

    • Bias and Fairness in Language Models

    • Privacy Concerns and Data Usage

    • Responsible Development and Deployment of LLMs

Quiz 5

6

3

• CourseSummarization
• MiniProjectPresentations
• Wrap up and Team feedback session

Quiz 6

6

CG Mock Presentations

6

Final Project Presentation