Machine learning techniques II BCS055 II SEMI-SUPERVISED vs REINFORCED LEARNING II B.Tech CSE 2025

Описание: Machine Learning Techniques: SEMI-SUPERVISED vs REINFORCED LEARNING (BCS055) 🤖This video is the essential second part of our Machine Learning Techniques series, directly covering the BCS055 syllabus for B.Tech CSE students (2025 batch). We conduct a deep, comparative analysis of two critical paradigms in machine learning: Semi-Supervised Learning (SSL) and Reinforcement Learning (RL).You will learn the fundamental concepts, hybrid methodologies, and real-world applications of both. We use clear examples—from classifying user data to the inner workings of modern AI like Gemini and autonomous vehicles—to ensure a strong conceptual grasp.Key Topics Covered:Understanding why Semi-Supervised Learning is the perfect bridge between Supervised and Unsupervised approaches.The high cost and time of manual labeling and how SSL solves this issue.The core concept of Reinforcement Learning, inspired by Behavioral Psychology (Reward and Penalty).How an ML Agent learns to maximize Cumulative Rewards through Trial and Error.Real-world applications of RL, including Autonomous Driving and AI Chatbot Refinement (RLHF).Master these techniques to prepare for your university exams and solidify your foundation in advanced ML concepts!⏱️ Video Chapters and Detailed Timestamps
TimestampTopic Summary
00:00:00 Introduction to Semi-Supervised Learning (SSL): A hybrid approach.
00:00:12 Example of SSL: Classifying a large dataset of names using only a small, partially-labeled subset (e.g., with religious or caste tags).
00:01:04 The core SSL Logic: Using a model trained on a small labeled set to classify the remaining large unlabeled set.
00:02:11 Formal Definition of SSL: Combining a Small Amount of Labeled Data with a Large Amount of Unlabeled Data.
00:02:30 Why SSL is necessary: Manual labeling is Expensive and Time-Consuming.
00:04:46 The Iterative SSL Process Flow: Classifying, Retraining, and Refining the classifier.
00:06:04 SSL in Healthcare: Training with few labeled X-rays to automatically classify thousands of others, saving expert effort.
00:08:18 Introduction to Reinforcement Learning (RL): Inspired by Behavioral Psychology.
00:08:35 RL Mechanism: Learning through Reward and Penalty (Analogy of personal motivation/kicks).
00:10:23 RL Definition: An Agent interacts with the Environment and receives feedback to Maximize Cumulative Rewards.
00:11:51 RL is different: Model learns via Trial and Error; there are No Fixed Correct Answers.
00:12:39 The RL Setup Diagram Explained: Agent, Action, Environment, Reward, Policy.
00:14:11 RL Example 1: RLHF (Reinforcement Learning with Human Feedback) in models like Gemini/ChatGPT.
00:15:41 RL Example 2: Autonomous Vehicles (Learning safe driving strategies in simulated environments).
00:17:05 RL Example 3: Robotics (Using gyroscopes to learn complex actions without explicit programming).
00:17:30 Conclusion: Summary of the four core learning methods.🏷️ Recommended Keywords & Hashtags (SEO)Keywords:Semi-Supervised Learning vs Reinforcement LearningBCS055Machine Learning TechniquesReinforcement Learning with Human Feedback (RLHF)ML Agent Reward PenaltyAutonomous Driving MLB.Tech CSE 2025Semi Supervised vs Reinforced
Hashtags:#machinelearning #ReinforcementLearning #SemiSupervised #BCS055 #BTechCSE #MLTechniques #RLHF #ComputerScience #AIJoin this channel to get access to perks:
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