Feedback of Learning

Duration: 9 min

This video lesson is available to enrolled students.

Return to /learn/BIHAR-STET-COMPUTER-SCIENCE-COMPLETE-COURSE/unit-13-artificial-intelligence/artificial-intelligence-artificial-neural-networks/introduction-to-artificial-neural-networks/asset-feedback-of-learning after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. About the course: About the Course
  2. Mathematical Aptitude: संख्या पद्धति / Number System, महत्तम समापवर्तक और लघुत्तम समापवर्त्य / HCF and LCM, Divisibility Rules, प्रतिशत / Percentage, अनुपात और समानुपात / Ratio and Proportion, औसत / Average, लाभ, हानि और बट्टा / Profit, Loss and Discount, समय और कार्य / Time and Work, घन क्षेत्रमिति / Solid Mensuration, साधारण और चक्रवृद्धि ब्याज / Simple and Compound Interest, समय, चाल और दूरी / Time, Speed and Distance, Age Problems, मिश्रण और एलिगेशन / Mixture and Alligation, Algebra, सरलीकरण, घातांक और करणी / Simplification, Indices and Surds, Pipe and Cistern, Trigonometry, Statistics, आँकड़ा निर्वचन / Data Interpretation, Arithmetic, समुच्चय, संबंध, फलन और विविक्त गणित / Sets, Relations, Functions and Discrete Mathematics, संख्या पद्धति और संख्या सिद्धांत / Number Systems and Number Theory, निर्देशांक ज्यामिति, सदिश और त्रिविमीय ज्यामिति / Coordinate Geometry, Vectors and 3D Geometry, बहुपद, समीकरण और सम्मिश्र संख्याएँ / Polynomials, Equations and Complex Numbers
  3. Logical Reasoning: श्रेणी / Series, कूट लेखन एवं कूट वाचन / Coding-Decoding, सादृश्यता एवं वर्गीकरण / Analogy and Classification, रक्त संबंध / Blood Relations, दिशा एवं दूरी / Direction and Distance, क्रम एवं रैंकिंग / Order and Ranking, अशाब्दिक तर्क / Non-Verbal Reasoning, बैठक व्यवस्था एवं पहेलियाँ / Seating Arrangement and Puzzles, Syllogisms, वेन आरेख, न्याय वाक्य एवं असमानता / Venn Diagrams, Syllogism and Inequalities, गणितीय संक्रियाएँ एवं लुप्त संख्या / Mathematical Operations and Missing Numbers, वर्णमाला एवं संख्या परीक्षण / Alphabet and Number Test, कथन आधारित तार्किक विवेचन / Statement-Based Reasoning, Analytical Reasoning (Counting Figures Reasoning), घन, पासा एवं आकृतियों की गिनती / Cubes, Dice and Figure Counting, कैलेंडर एवं घड़ी / Calendar and Clock, Clock
  4. General Knowledge: वैज्ञानिक उपलब्धियाँ और अनुसंधान / Scientific Achievements and Research, Chemistry, Biology, History, Geography, Polity, Indian Economy, समसामयिकी / Current Affairs, Static General Knowledge, राज्य विशेष सामान्य ज्ञान / State-Specific General Knowledge
  5. Art of Teaching: शिक्षण-अधिगम प्रक्रिया एवं कक्षा प्रबंधन / Teaching-Learning Process and Classroom Management, Learners & Learning Process, Factors Affecting Teaching, शिक्षण विधियाँ एवं उपागम / Teaching Methods and Approaches, सूक्ष्म शिक्षण एवं कक्षा संप्रेषण / Microteaching and Classroom Communication, शिक्षण सहायक सामग्री एवं शैक्षिक तकनीकी / Teaching Aids and Educational Technology, आकलन एवं मूल्यांकन / Assessment and Evaluation, शिक्षक: गुण, व्यावसायिक नैतिकता एवं विकास / Teacher: Qualities, Professional Ethics and Development, शैक्षिक नीतियाँ, अधिनियम एवं प्रशासन / Educational Policies, Acts and Administration, पाठ्यचर्या, कला एवं सह-शैक्षिक क्षेत्र / Curriculum, Arts and Co-curricular Areas, निर्देशन एवं परामर्श / Guidance and Counselling, दिव्यांग बच्चों की शिक्षा / Education of Children with Disabilities, Micro-Teaching & Teaching Skills, पाठ योजना / Lesson Planning
  6. Environmental Science: Environment
  7. Unit 1 | Digital Electronics: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation
  8. Unit 2 | COA: Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Instr Formats & Modes, Control Unit Design, Register Transfer and Microoperations, Programming the Basic Computer
  9. Unit 3 | C Programming: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling
  10. Unit 3 | Data Structures: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  11. Unit 4 | Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  12. Unit 5 | Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management
  13. Unit 6 | Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Transaction Management, Concurrency Control
  14. Unit 7 | Computer Networks: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer
  15. Unit 8 | Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  16. Unit 9 | Object Oriented Programming: OOPS with C++
  17. Unit 10 | Web Development: HTML Basics, CSS, JavaScript-Basics, XML, Node.js, Git & Github, PHP
  18. Unit 11 | Theory Of Computation: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines
  19. Unit 12 | IoT: Emerging Tech Trends, Cloud Computing, कंप्यूटर नेटवर्क / Computer Networks
  20. Unit 13 | Artificial Intelligence: Approaches to AI, Search Algorithms, Game Playing, Knowledge Representation, Natural Language Processing, Artificial Neural Networks, Genetic Algorithms
  21. Unit 14 | E-Commerce: Electronic Data Interchange and E-Commerce, Role of Internet and Web Services, Security in Mobile and Wireless Computing, Physical Security and Cryptography Basics, Cyber Safety and Security
  22. Unit 15 | Multimedia: Multimedia
  23. Unit 16 | Python: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis, DSA using Python
  24. Full Length Mock Tests — General Paper:
  25. Full Length Mock Tests — Computer Science (Paper II):
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video lecture provides a comprehensive overview of the primary types of machine learning, categorized by the nature of feedback available to the learning agent. The instructor begins by listing the three main types—Unsupervised, Supervised, and Reinforcement Learning—on a slide titled "Feedback to learn from." She then expands this list to include Semi-Supervised Learning. Throughout the session, she annotates the slides with handwritten notes to clarify concepts, such as linking Unsupervised Learning to "Self Study" and "Clustering," and Supervised Learning to "Classification." The lecture delves into specific definitions, using examples like a taxi agent for Unsupervised Learning and image recognition for Supervised Learning. It concludes by explaining Reinforcement Learning through the concept of rewards and punishments, and finally defines Semi-Supervised Learning as a hybrid approach combining labeled and unlabeled data.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The session opens with a slide titled "Feedback to learn from," which lists three bullet points: Unsupervised Learning, Supervised Learning, and Reinforcement Learning. The instructor underlines the word "feedback" and adds a fourth point, "Semi learning," to the list. She begins annotating the slide to explain the feedback mechanism for each type. Next to Unsupervised Learning, she writes "Self Study [No Teacher]" and draws an arrow to "Clustering." Above Unsupervised Learning, she writes "Master [Baby learns]." For Supervised Learning, she writes "Guide [model]" and an arrow to "Classification." For Reinforcement Learning, she writes "Reward & punish." This section establishes the foundational categories of learning based on the presence or absence of a teacher or feedback signal.

  2. 2:00 – 5:00 02:00-05:00

    The slide changes to a detailed definition of "Unsupervised Learning." The text states that the agent learns patterns in the input even though no explicit feedback is supplied, noting there is a minimum of human supervision. The instructor underlines the phrase "no explicit feedback supplied" and "minimum of human supervision." The slide identifies "clustering" as the most common task, defined as detecting potentially useful clusters of input examples. An example is given of a taxi agent developing concepts of "good traffic days" and "bad traffic days" without labeled examples. The instructor writes "Bad traffic" on the screen to emphasize the example. This segment focuses on the autonomous nature of Unsupervised Learning where the system finds structure in data without guidance.

  3. 5:00 – 8:46 05:00-08:46

    The lecture transitions to "Supervised Learning," defined as the agent observing example input-output pairs to learn a mapping function. The instructor writes "Model -> image -> Bus" and draws a simple bus diagram to illustrate the concept. She also writes "Classification" and lists score ranges like "90-100 -> A" and "80-89 -> B" to explain grading or categorization. The slide then moves to "Reinforcement Learning," where the agent learns from a series of reinforcements—rewards or punishments. The instructor writes "learn from consequences" on the screen. An example of a taxi agent not receiving a tip is used to illustrate negative reinforcement. Finally, the slide covers "Semi-Supervised Learning," described as combining a small amount of labeled data with a large amount of unlabeled data. The instructor writes "Semi -> Small amount Labeled + Unlabeled" and draws a diagram showing a small labeled portion versus a large unlabeled portion, visually representing the hybrid nature of this learning type.

The video systematically breaks down machine learning paradigms by analyzing the feedback loop. It starts with a broad classification, distinguishing between learning with a teacher (Supervised), without a teacher (Unsupervised), and through trial and error (Reinforcement). The instructor uses handwritten annotations to reinforce these distinctions, linking Unsupervised Learning to clustering and self-study, and Supervised Learning to classification and guided models. The progression moves from the lack of feedback in Unsupervised Learning to the explicit input-output pairs in Supervised Learning, then to the reward-based feedback in Reinforcement Learning. The lesson concludes by introducing Semi-Supervised Learning as a middle ground, effectively bridging the gap between fully labeled and fully unlabeled datasets. This structured approach helps students understand the spectrum of learning algorithms based on data availability and feedback mechanisms.

Loading lesson…