UNIT - V Analytical Learning-1- Introduction, learning with perfect domain theories: PROLOG-EBG, remarks on explanation-based learning, explanation-based learning of search control knowledge. Analytical Learning-2-Using prior knowledge to alter the search objective, using prior knowledge to augment search operators. Combining Inductive and Analytical Learning - Motivation, inductive-analytical approaches to learning, using prior knowledge to initialize the hypothesis.
Mam can you explain about machine learning topics like Generalization bound Locality sensitive hashing Non parametric regression Ensemble learning Construction of regression tree Silhouette algorithm Active and passive reinforcement Adaptive dynamic programming Direct utility estimation In machine learning
Thank you so much! This was a great explanation ♥️ Can you please make a video on temporal difference learning zero and one, ( TD (0) and TD(1) ) and how they can be used in random walk?
Mam, your are simply going with concept .But your are not explaining examples .Better take one example and explain nah. Meanwhile machine learnin subject is some what technical (examples) based concepts ,make some new examples. Meanwhile ur explanation is very good and its better to understand ur explanation .But sorry mam to say this starting intro is not useful in my opinion it takes more time length for video bettr cut that clip about exam schedule , clg name etc. Thank you
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UNIT - V
Analytical Learning-1- Introduction, learning with perfect domain theories: PROLOG-EBG, remarks
on explanation-based learning, explanation-based learning of search control knowledge.
Analytical Learning-2-Using prior knowledge to alter the search objective, using prior knowledge to
augment search operators.
Combining Inductive and Analytical Learning - Motivation, inductive-analytical approaches to
learning, using prior knowledge to initialize the hypothesis.
Episodic :sequential??
mam can you share all ml notes ,so that it could be easy
Thank you mam ..it was very useful to us ..Mam can u share ml notes plssss so that we can read it easily
Mam can you explain about machine learning topics like
Generalization bound
Locality sensitive hashing
Non parametric regression
Ensemble learning
Construction of regression tree
Silhouette algorithm
Active and passive reinforcement
Adaptive dynamic programming
Direct utility estimation
In machine learning
Iam having exams from 10 th July can you please make it
Jntuh baditulu antaru ra babu😅
Can you do a video on non deterministic rewards and actions
Iam from malla Reddy college of engineering for women
you did not explain the safe to stock
Thank you so much! This was a great explanation ♥️ Can you please make a video on temporal difference learning zero and one, ( TD (0) and TD(1) ) and how they can be used in random walk?
Sure I’ll try
Adaptive dynamic programming in machine learning explanation mam
Mam, your are simply going with concept .But your are not explaining examples .Better take one example and explain nah. Meanwhile machine learnin subject is some what technical (examples) based concepts ,make some new examples. Meanwhile ur explanation is very good and its better to understand ur explanation .But sorry mam to say this starting intro is not useful in my opinion it takes more time length for video bettr cut that clip about exam schedule , clg name etc.
Thank you
Mam if possible can u pls start data mining tutorial as most of the jntuh students have it in current semester
i have seen so many jntuh people commenting asking for videos, is the clg not taking even 10% classes?
@@MSCAIMLRBRITHANYA ante adhi....
ive exams nearby MAchine learning TKR college JNTUH autonomous
topic adaptive dynamic progrming sub ai clg cmrit date next month 10
15 june Punjab technical university
I have exms in November 8
aaj hi hai.... lol 10/07/23 🙂
Hey, your content is wrong!! Correct it
Please remove the disgusting intro music 🤮
I have exms in November 8