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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

Course Curriculum

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
Certification 00:00:00

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