Machine Learning Fundamentals Quiz

Machine Learning Introduction

• Machine learning (ML) is a subset of Artificial Intelligence (AI) focusing on building systems that learn from data without explicit programming.

• The goal is to enable computers to learn from experience and adapt their behavior over time.

• Key components of machine learning include data, algorithms, and experience.

Course Objectives

• Understand machine learning fundamentals: core concepts, types of learning (e.g., supervised, unsupervised, reinforcement), and their applications.
• Apply supervised learning algorithms: becoming proficient with algorithms like linear regression, decision trees, and support vector machines (SVM) for tasks such as classification and regression.
• Explore unsupervised learning: learn to identify patterns in data using clustering techniques (e.g., k-Means, hierarchical) and dimensionality reduction (e.g., PCA).
• Master neural networks and deep learning: understand and implement neural networks and advanced architectures (e.g., CNNs, RNNs) for tasks like image recognition and sequence modeling.
• Evaluate and tune model performance: assess models using metrics (accuracy, precision) and optimize through cross-validation and hyperparameter tuning.
• Data preprocessing and feature engineering: develop skills to clean, preprocess, and engineer features from raw data to improve model accuracy and performance.
• Hands-on experience with ML tools: practice using tools (e.g., Python, Scikit-learn, TensorFlow, Keras) through real-world projects.
• Ethical and fair use of machine learning: understand ethical implications (e.g., biases, fairness, privacy concerns) in model development.
• Introduction to reinforcement learning: learn the basics of reinforcement learning, including Q-learning and policy optimization for decision-making.
• Deploy ML models into production: gain experience in deploying machine learning models into real-world environments using APIs and cloud platforms.

Contents Chapter Outline

• Introduction to Machine Learning
• Supervised Learning Fundamentals
• Regression Algorithms
• Classification Algorithms
• Decision Trees and Ensemble Learning
• Unsupervised Learning Basics
• Neural Networks and Deep Learning
• Natural Language Processing (NLP) and Sequence Models
• Reinforcement Learning
• Model Evaluation and Tuning
• Model Deployment and Applications

1. What is Machine Learning?

• Formal definition: (Tom M. Mitchell) "A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E."
• Examples: spam email filtering, recommendation systems.

Problem Setup

• One definition of machine learning is a process, where a computer program improves its performance on a task using experience (e.g., data or examples).
• The task, experience, and performance are separate elements.

1- Task

• Task: The problem a program is meant to solve.
• Machine learning tackles tasks too complex for traditional, fixed programs.

1- Task (Further Detail)

• Classification: specifying which category an input belongs to (e.g., colors, good/bad credit risks)
• Regression: predicting a numerical value given input (e.g., integers, real numbers, vectors)
• Transcription: converting unstructured data into discrete, textual form (e.g., OCR)
• Machine translation: converting symbols from one language to another.

2- Experience

• Experience: the "data" (examples) a program uses to improve performance.
• A dataset is a collection of examples
• Each example is a set of features measured from an object/event.

3- Performance measure

• Performance measure: quantitative measure for performance (e.g. accuracy and error rate).
• It should be specific to the given task.

When do we use machine learning?

• When human expertise is lacking.
• When we can't explain a person's expertise.
• When we're working with large amounts of data.

2. Types of Machine Learning

• Supervised learning: algorithms trained on labeled data. Each data point has a corresponding label/outcome.
• Example: Predicting house prices, based on size, location etc.
• Algorithms: Linear regression, decision trees, support vector machines (SVM).
• Unsupervised learning: algorithms trained on data without explicit labels. The goal is to find patterns or structure in the data.
• Example: Grouping customers by purchasing behavior.
• Algorithms: K-means clustering, Hierarchical clustering, PCA.

Additional Types of Machine Learning

• Semi-supervised learning: A mixture of supervised and unsupervised learning.
• Reinforcement learning: An agent learns by interacting with an environment , receiving feedback (rewards or penalties).
• Goals are to maximize a reward function.
• Algorithms include Q-learning, Deep Q-Networks (DQN).

Supervised Learning Paradigm

• Training inputs, labels/outcomes.
• Algorithms map input features to outputs/labels.
• Testing- input features are fed into the algorithm.
• Machine learning algo delivers an output value

Supervised Learning

• Aims to predict/classify an outcome based upon input
• Input - Output relationship assumed
• Regression: predict continuous valued output (e.g. price).
• Classification: predict discrete valued output (e.g. 0 or 1).

Unsupervised Learning

• Used to derive structure/patterns without known outcomes.
• Aims to find hidden relationships among data inputs.
• Clustering: Used to group data into clusters based on similarities.

Unsupervised Learning Applications

• Using microarray data to cluster individuals based on gene expression similarities.

3. Why is Machine Learning Important?

• Data-driven decision making
• Automation
• Adaptability
• Ubiquity

4. Key Terminology in Machine Learning

• Features (or Attributes): measurable properties of data (e.g., a house's size, number of bedrooms).
• Labels/Targets: outputs the model predicts (e.g., house price).
• Training Data: Used to train models/ algorithms.
• Test Data: Evaluates models on unseen data.
• Validation Data: used to adjust model parameters without overfitting.

5. ML Workflow and Pipeline

• Problem definition: understand the problem and requirements.
• Data collection: Gathering relevant data (databases, APIs, scraping).
• Data preprocessing: Cleaning, normalizing, encoding data.
• Model selection: choosing an appropriate algorithm.
• Model training: applying data to the chosen algorithm.
• Model evaluation: determining model performance.
• Model tuning: adjusting parameters to refine performance.
• Model deployment: integrating the model into a wider system.

6. Common Applications of Machine Learning

• Image and Speech recognition
• Healthcare
• Natural Language Processing (NLP)
• Financial Services
• Recommendation Systems

7. Common Algorithms in Machine Learning

• Linear regression: predicting continuous outcomes using a linear equation.
• Logistic Regression: for binary classification tasks.
• Decision tress: used for both classification and regression, using branching based on feature values.
• k-Nearest Neighbors (k-NN): classifies a data point based on the majority class of its nearest neighbors.
• Support Vector Machines (SVM): a powerful classification algorithm that finds the optimal hyperplane to separate data into different classes.

8. Limitations and Challenges of Machine Learning

• Data dependency
• Overfitting
• Interpretability
• Ethical concerns

References

• Peter Flach, Machine Learning.
• T. Mitchell, Machine Learning.
• John D. Kelleher, Fundamental of Machine Learning for predictive Data Analytic.