L13: A typical supervised machine learning setup | models, & loss functions explained

Welcome to Lecture 13 of the course "Deep Learning" by Prof. Mitesh M.Khapra Full Course: https://study.iitm.ac.in/ds/course_pages/BSCS3004.html Video Overview This lecture introduces the essential setup of supervised machine learning in a clear and practical way. We begin by explaining how to frame real world problems such as predicting movie box office collections or oil mining output into a structured data format with inputs X and outputs Y. The lecture covers why we need models to approximate the relationship between inputs and outputs and the role of parameters within these models. We also discuss how learning algorithms help us find the best parameter values to improve predictions. Additionally, the concept of loss functions is introduced, explaining how minimizing these functions drives the learning process. Throughout the session, data setup, models, and algorithms are explained using practical examples to build your foundational understanding of supervised learning. About IIT Madras' online Bachelor of Science programme IIT Madras offers four-year BS programmes that aim to provide quality education to all, irrespective of age, educational background, or location. The BS programme has multiple levels, which provide flexibility to students to exit at any of these levels. Depending on the courses completed and credits earned, the learner can receive a Foundation Certificate from IITM CODE (Centre for Outreach and Digital Education), Diploma(s) from IIT Madras, or BSc/BS Degrees from IIT Madras. For more details, Visit: https://www.iitm.ac.in/academics/study-at-iitm/non-campus-bs-programmes #SupervisedLearning #MachineLearning #Model #LossFunction #Optimization #Algorithm #TrainingData #Parameters #Prediction #DataScience #AI #ModelEvaluation #MLPipeline #Overfitting #Underfitting #HyperparameterTuning #MLModelTraining #AIApplications #LearningCurve #PredictiveModeling