Unit I - Introduction to Machine Learning

Overview

The "Introduction to Machine Learning" unit provides foundational knowledge about machine learning (ML), covering key concepts, paradigms, and models. This unit introduces what machine learning is, how it differs from traditional programming, and its relationship with AI and Data Science. It also explores various learning paradigms such as supervised, unsupervised, semi-supervised, and reinforcement learning, along with different types of models and techniques like dimensionality reduction using PCA and LDA.

Key Topics

Definition and Real-life Applications of Machine Learning
Comparison with Traditional Programming
ML vs AI vs Data Science
Learning Paradigms: Supervised, Unsupervised, Semi-supervised, Reinforcement Learning
Models of Machine Learning: Geometric, Probabilistic, Logical, Grouping and Grading, Parametric and Non-parametric Models
Feature Transformation: PCA and LDA

Resources

Books:
- "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
- "Pattern Recognition and Machine Learning" by Christopher M. Bishop
- "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Online Courses:
- Coursera's "Machine Learning" by Andrew Ng
- edX's "Introduction to Machine Learning" by MIT
- Udemy's "Python for Data Science and Machine Learning Bootcamp"
Research Papers:
- "A Few Useful Things to Know About Machine Learning" by Pedro Domingos
- "Deep Learning" by Yann LeCun, Yoshua Bengio, and Geoffrey Hinton
Websites:
- Scikit-learn Documentation: Scikit-learn
- TensorFlow Documentation: TensorFlow
- Machine Learning Mastery: Machine Learning Mastery

Syllabus Topics

Introduction to Machine Learning
- What is Machine Learning?
  - Definition and overview
  - Historical background (Arthur Samuel's definition)
  - Examples and scenarios where ML is applied
- Real-life ApplicationsReal-life ApplicationsApplications in Various Sectors: Healthcare:** * Disease Diagnosis: ML models are used to diagnose diseases from medical images (e.g., X-rays, MRIs) or predict disease outbreaks. For example, deep learning models can identify cancerous tissues from mammograms with high accuracy. * Personalized Medicine: ML algorithms help in creating personalized treatment plans based on patient data, genetic information, and previous treatment outcomes. Finance:** * Fraud Detection: Banks and financial i
  - Applications in healthcare, finance, retail, etc.
  - Case studies demonstrating ML impact
- Comparison with Traditional ProgrammingComparison with Traditional ProgrammingDifferences Between Traditional Programming and ML-Based Approaches: Traditional Programming:** * Approach: The programmer provides explicit instructions to the computer to perform a task. The logic and rules are predefined by the programmer based on a clear understanding of the problem. * Handling Complexity: Well-suited for tasks with deterministic rules and fixed logic. Complex scenarios require extensive coding and maintenance. * Data Dependency: Does not inherently depend on large am
  - Differences between traditional programming and ML-based approaches
  - Use cases where each approach is most effective
- ML vs AI vs Data ScienceML vs AI vs Data ScienceML vs AI vs Data Science Definitions and Distinctions: Artificial Intelligence (AI):** * AI refers to the broader concept of machines being able to carry out tasks in a way that we would consider “smart.” It is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, problem-solving, perception, and language understanding. Machine Learning (ML):** * ML is a subset of AI that enables machines to improve at tasks wi
  - Definitions and distinctions between Machine Learning, Artificial Intelligence, and Data Science
  - How these fields overlap and differ
  - Examples demonstrating the distinctions
Learning Paradigms
- Learning Tasks
  - Descriptive Tasks: Understanding patterns and relationships within data (e.g., clustering)
  - Predictive Tasks: Making predictions based on input data (e.g., regression, classification)
- Supervised Learning
  - Definition and process
  - Examples (e.g., spam detection, image classification)
  - Advantages and disadvantages
- Unsupervised Learning
  - Definition and process
  - Examples (e.g., customer segmentation, anomaly detection)
  - Advantages and disadvantages
- Semi-supervised Learning
  - Combination of supervised and unsupervised learning
  - Real-world applications (e.g., text classification with limited labeled data)
- Reinforcement Learning
  - Agent-environment interaction framework
  - Key concepts: rewards, policy, value function
  - Applications in robotics, game AI, etc.
Models of Machine Learning
- Geometric Models
  - Overview of geometric representations in ML
  - Linear models (e.g., linear regression, SVM)
  - Distance-based models (e.g., k-NN, clustering algorithms)
- Probabilistic Models
  - Probability theory in ML (e.g., Naive Bayes, Bayesian networks)
  - Key concepts: likelihood, prior, posterior
- Logical Models
  - Rule-based systems (e.g., decision trees, expert systems)
  - Advantages and limitations of logical models
- Grouping and Grading Models
  - Clustering techniques (e.g., K-means, hierarchical clustering)
  - Ranking and scoring models (e.g., ordinal regression, RankNet)
- Parametric and Non-parametric Models
  - Differences and when to use each
  - Examples of parametric models (e.g., logistic regression)
  - Examples of non-parametric models (e.g., decision trees, kernel methods)
Feature Transformation
- Dimensionality Reduction Techniques
  - Principal Component Analysis (PCA)
    - Definition and purpose
    - Steps in PCA
    - Use cases (e.g., noise reduction, data visualization)
  - Linear Discriminant Analysis (LDA)
    - Definition and purpose
    - Comparison with PCA
    - Use cases (e.g., classification tasks)

Previous Year Questions (PYQs)

Comparison of Concepts
- Compare Machine Learning with Traditional Programming.
  - Explain the differences between traditional programming and machine learning, highlighting their respective approaches, handling of complexity, data dependency, adaptability, rule extraction, maintenance, problem types, development effort, and feedback loop.
- Compare ML vs AI vs Data Science.
  - Discuss how machine learning, artificial intelligence, and data science differ, and provide examples where each field is applied.
Learning Paradigms
- Explain Supervised Learning with its Advantages and Disadvantages.
  - Describe the process of supervised learning, its benefits, and drawbacks, and provide examples of its applications.
- Differentiate Supervised and Unsupervised Learning Techniques.
  - Compare the approaches, use cases, and advantages of supervised vs. unsupervised learning.
- Explain Reinforcement Learning and Briefly Discuss its Applications.
  - Provide an overview of reinforcement learning, its key components, and real-world applications, such as robotics and game AI.
Machine Learning Models
- Describe Parametric and Non-parametric Machine Learning Models.
  - Define parametric and non-parametric models, explain their differences, and discuss when to use each type.
- Elaborate Grouping and Grading Models.
  - Discuss the purpose, algorithms, and applications of grouping (e.g., clustering) and grading models (e.g., ranking, scoring).
- Elaborate Random Forest Regression.
  - Explain the concept of random forest regression, how it works, and its advantages in predictive modelling.
Feature Transformation
- What is Dimensionality Reduction? Explain any One Dimensionality Reduction Technique.
  - Define dimensionality reduction and discuss techniques such as PCA or LDA, including their applications.
- What is Principal Component Analysis (PCA), and When is it Used?
  - Provide an overview of PCA, its purpose, the steps involved, and its applications in reducing data dimensionality while preserving variance.
Evaluation Metrics
- Explain Techniques to Reduce Overfitting in Machine Learning.
  - Discuss the causes of overfitting and strategies to mitigate it, such as regularization, cross-validation, and pruning.
- Write a Note on Mean Squared Error (MSE) and Mean Absolute Error (MAE).
  - Define MSE and MAE, explain their roles as evaluation metrics in regression analysis, and compare their sensitivity to outliers.
- Explain Elastic Net Regression in Machine Learning.
  - Describe the elastic net regression technique, how it combines ridge and lasso regression, and its application in handling multicollinearity.

Lecture Notes

Lecture Notes 1

Case Studies

Case Study 1:

Exercises and Assignments

Active Recall Questions

Mind Maps

Mind Map 1:

Keywords and Flashcards

Learning Mnemonic:
Flashcard Set 1:

Summary

DS-U3-S-NoteDS-U3-S-NoteKey Takeaways from Unit III - Data Analytics Lifecycle 1. Data Analytics Lifecycle Overview: * Phases: * Discovery: Understanding business problems, identifying data sources, and formulating hypotheses. * Data Preparation: Collecting, cleaning, and transforming data to ensure quality and usability. * Model Planning: Conducting exploratory data analysis (EDA), selecting modeling techniques, and planning the modeling approach. * Model Building: Developing and training pred
Key Takeaways:
Next Steps:
Condense Notes: DS-U3-Short SummaryDS-U3-Short SummaryCondensed Notes: Unit III - Data Analytics Lifecycle 1. Data Analytics Lifecycle Overview Phases:** * Discovery: * Understand business problems and objectives. * Identify data sources and formulate initial hypotheses. * Data Preparation: * Collect, clean, and transform data for analysis. * Ensure data quality and consistency. * Model Planning: * Conduct exploratory data analysis (EDA). * Select appropriate modeling techniques and tools. * Model Building: * D

Review Checklist

Revisit lecture notes
Practice exercises
Review flashcards
Engage with case studies
Test understanding with Active Recall Questions
Update mind map as needed