Machine Learning PCCST503 Semester 5 KTU CS 2024 Scheme - Dr Binu V P

About Me  - Dr Binu V P Syllabus Machine Learning PCCST503 KTU 2024 Scheme Model Question Paper Machine Learning PCCST503 Semester 5 KTU CS 2024 Scheme Do the programs to better understand the concept. Machine Learning Lab PCCSL508 Module -I Introduction to Machine Learning Introduction to ML Distinguishing Machine Learning from Traditional Programming Machine Learning Paradigms Supervised UnSupervised Semi-Supervised Reinforcement Stages of Machine Learning: From Data Collection to Deployment Parameter Estimation Introduction to Parameter Estimation in Machine Learning Maximum Likelyhood Estimation.(MLE) Maximum A Posteriori Estimation.(MAP) Numerical Problems MLE and MAP Supervised Learning Feature Representation and Problem Formulation Role of loss functions and optimization  Regression Introduction to Regression   Linear regression with one variable  (  lab experiment ) Example Problems- simple linear regression - University Questions Evaluation Metri...

  📘 Introduction to Machine Learning (ML) 🔹 What is Machine Learning? Machine Learning is a subfield of Artificial Intelligence that focuses on developing algorithms that allow computers to learn patterns from data and make decisions or predictions without explicit programming . It focuses on developing algorithms and models that allow machines to: Identify patterns in data Make decisions with minimal human intervention Predict future outcomes based on past experiences By continuously learning from new data, machine learning systems become more accurate and efficient over time. 📌 Formal Definition  A computer program is said to learn from experience E with respect to some task T and performance measure P if its performance at task T , as measured by P , improves with experience E . 🔹 Why Machine Learning? Traditional programming: Input + Program → Output Machine Learning paradigm: Input + Output → Model (learned program) 👉 Instead of writing...

  📘 Distinguishing Machine Learning from Traditional Programming 🔹 1. Fundamental Idea 🧾 Traditional Programming In conventional programming, developers write explicit instructions for the system to follow. The programmer explicitly writes rules/logic System follows deterministic instructions Input data is processed using predefined rules The output is based strictly on those rules 👉 Flow: Input + Program → Output 🤖 Machine Learning In machine learning, the system learns patterns from data instead of being explicitly programmed. Programmer provides  data + learning algorithm The model is trained using data The system  learns rules automatically from data It makes predictions or decisions based on learned patterns 👉 Flow: Input + Output → Learning Algorithm → Model Model + New Input → Predicted Output 🔹 2. Key Difference in Approach Aspect      Traditional Programming Machine Learning Input method Logic creation   ...

  📘 Reinforcement Learning (RL) 🔹 Definition Reinforcement Learning is a type of Machine Learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties . RL focuses on how agents learn through trial and error to maximize cumulative reward . 🔹Basic Idea (Intuition) The system learns by doing No labeled data is provided Feedback is delayed and indirect 📌 Simple Analogy: 👉 Training a dog: Correct action → reward Wrong action → punishment 👉 Over time, the dog learns optimal behavior. 🔹 Key Components of RL (Very Important)  RL consists of: 🟦 1. Agent The learner or decision-maker 🟩 2. Environment The world in which the agent operates 🟨 3. State (S) Current situation of the agent 🟥 4. Action (A) Possible decisions the agent can take 🟪 5. Reward (R) Feedback from environment (positive/negative) 👉 These components interact continuously. ...

  📘 Semi-Supervised Learning (SSL) 🔹 Definition Semi-Supervised Learning is a machine learning paradigm that uses: Small amount of labeled data Large amount of unlabeled data 👉 It combines ideas from: Supervised Learning (with labels) Unsupervised Learning (without labels) SSL aims to learn a function that predicts outputs using limited labeled data and abundant unlabeled data . Example of Semi-Supervised Learning Scenario: Imagine you’re building a model to classify images of animals such as: Dogs Cats Birds Labeling thousands of images manually would require significant time and effort. However, you have access to millions of unlabeled images . Approach: With semi-supervised learning: You start with a small set of labeled images to train the model The model then predicts labels for the large set of unlabeled data These predictions help the model learn better patterns and improve accuracy Outcome: ➡️ The model improves its performan...

  📘 Unsupervised Learning 🔹 Definition Unsupervised Learning is a type of Machine Learning where the model is trained on unlabeled data , i.e., data without predefined outputs. 👉 Goal: Discover hidden patterns, structures, or relationships in data. This type of learning is particularly useful for discovering clusters in data, detecting anomalies, and finding associations. Techniques like clustering, association rule mining, and dimensionality reduction allow for deeper insights into complex datasets. It's widely used in customer segmentation, fraud detection, and market basket analysis. 🔹 Key Idea Given only input data: X = { x 1 , x 2 , x 3 , . . . , x n } X = \{x_1, x_2, x_3, ..., x_n\} 👉 The model tries to: Group similar data Identify patterns Reduce dimensionality 🔹 Characteristics No labeled outputs No “teacher” Exploratory in nature Used for pattern discovery Pic Courtesy: database town 🔹Types of Unsupervised Learning 🟦 A. Clustering ...