The [course_title] course is a machine learning related course that coves the basic theory, algorithms, and applications of machine learning of MI.
Topics included the learning problem, feasibility of learning, the linear model, error and noise, training vs, testing, and the theory of generalisation. You will learn whether a machine can learn or not. If yes, then how? The course also explains the VC Dimension, Bias-Variance Tradeoff, Neural Networks, Overfitting, Regularization, Validation, Kernel Methods, Radial Basis Functions, and Three Learning Principles.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: California Institute of Technology
|Lecture 01 – The Learning Problem||01:27:00|
|Lecture 02 – Is Learning Feasible?||01:17:00|
|Lecture 03 -The Linear Model I||01:20:00|
|Lecture 04 – Error and Noise||01:18:00|
|Lecture 05 – Training Versus Testing||01:17:00|
|Lecture 06 – Theory of Generalization||01:18:00|
|Lecture 07 – The VC Dimension||01:14:00|
|Lecture 08 – Bias-Variance Tradeoff||01:17:00|
|Lecture 09 – The Linear Model II||01:27:00|
|Lecture 10 – Neural Networks||01:25:00|
|Lecture 11 – Overfitting||01:20:00|
|Lecture 12 – Regularization||01:15:00|
|Lecture 13 – Validation||01:26:00|
|Lecture 14 – Support Vector Machines||01:14:00|
|Lecture 15 – Kernel Methods||01:18:00|
|Lecture 16 – Radial Basis Functions||01:22:00|
|Lecture 17 – Three Learning Principles||01:16:00|
|Lecture 18 – Epilogue||01:09:00|
|Submit Your Assignment||00:00:00|
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