UNIT - III Bayesian learning - Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum Likelihood and least squared error hypotheses, maximum likelihood hypotheses for predicting probabilities, minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve Bayes classifier, an example: learning to classify text, Bayesian belief networks, the EM algorithm. Computational learning theory - Introduction, probably learning an approximately correct hypothesis, sample complexity for finite hypothesis space, sample complexity for infinite hypothesis spaces, the mistake bound model of learning. Instance-Based Learning- Introduction, k-nearest neighbour algorithm, locally weighted regression, radial basis functions, case-based reasoning, remarks on lazy and eager learning.
Thank you for your lectures mam they are really helpful to prepare for sem exam....Hope you will be posting videos on unit 3,4,5 also. we have ML exam on 24 aug (JNTUH) syllabus
hello mam....ur videos are very useful to us... i am requesting that u can make videos on WEB TECHNOLOGIES.... by 3rd jan of 2023.. clg name pscmr , vijayawada ...i am student of jntuk university..i hope u will make videos very soon as possible..thank you mam..
Thank you for good Explanation ❤️.Can u make videos on scripting language(JNTUH) SREE DATTHA GROUP OF INSTITUTIONS Exam on 28th.No need full syllabus.only 2 units or 3 units
if you don't mind mam please try to explain slowly I have been watching your viedos the whole day but everywhere I had felt like you are in a hurry to finish it off quickly so sometimes it is getting tough to catch your flow.
hello ma'am ,even I'm a jntuh student ,if unit 3 is easy compared to other units then fine else could you share the videos of unit 4 & 5 if at all possible since they are comparatively smaller than unit 3 is what I felt so that we'll be able to attempt the exam with ease on 24th aug . just for reference UNIT - III Bayesian learning - Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum Likelihood and least squared error hypotheses, maximum likelihood hypotheses for predicting probabilities, minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve Bayes classifier, an example: learning to classify text, Bayesian belief networks, the EM algorithm. Computational learning theory - Introduction, probably learning an approximately correct hypothesis, sample complexity for finite hypothesis space, sample complexity for infinite hypothesis spaces, the mistake bound model of learning. Instance-Based Learning- Introduction, k-nearest neighbour algorithm, locally weighted regression, radial basis functions, case-based reasoning, remarks on lazy and eager learning. UNIT- IV Genetic Algorithms - Motivation, Genetic algorithms, an illustrative example, hypothesis space search, genetic programming, models of evolution and learning, parallelizing genetic algorithms. Learning Sets of Rules - Introduction, sequential covering algorithms, learning rule sets: summary, learning First-Order rules, learning sets of First-Order rules: FOIL, Induction as inverted deduction, inverting resolution. Reinforcement Learning - Introduction, the learning task, Q-learning, non-deterministic, rewards and actions, temporal difference learning, generalizing from examples, relationship to dynamic programming. 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 if possible... Please make videos on the subject 'discrete mathematics' according to the r15 syllabus of JNTUA 🙏🙏🙏🙏🙏 I have exams by this month end...
Hello mam. I am pursuing masters in USA and I have my machine learning exam within 15 days. So can you please let me know the Important concepts to prepare up for my examination ?
![ILLSLICK - KILLSHOT REMIX [FULL VERSION]](http://i.ytimg.com/vi/RIK8RrqIAzE/mqdefault.jpg)
Like if u r here one night before exams😂❤
me 😬😬
1 hour before exam
I see this theorem now and i no need any practice thank you my exam in with in 20 mins thanks a lot mam... Thanks 🙏🙏🌹🌹
UNIT - III
Bayesian learning - Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum
Likelihood and least squared error hypotheses, maximum likelihood hypotheses for predicting
probabilities, minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve
Bayes classifier, an example: learning to classify text, Bayesian belief networks, the EM algorithm.
Computational learning theory - Introduction, probably learning an approximately correct hypothesis,
sample complexity for finite hypothesis space, sample complexity for infinite hypothesis spaces, the
mistake bound model of learning.
Instance-Based Learning- Introduction, k-nearest neighbour algorithm, locally weighted regression,
radial basis functions, case-based reasoning, remarks on lazy and eager learning.
You are absolutely good teacher I like to watch your videos 🥰🥰🥰🥰🥰
Thank you for your lectures mam they are really helpful to prepare for sem exam....Hope you will be posting videos on unit 3,4,5 also. we have ML exam on 24 aug (JNTUH) syllabus
Suuppeerr madam
You're voice unconsiace
"I love you madam"
Thank you so much Mam for such a great explanation of all the concepts.. ✨🙌🏻🙏
Today my exam 😅
Thank you so much you have explained the equations in a very simple way.
Tq mam mee valle iroju e ans attempt chesa🙏
Your explanation is just fabulous!!
Thanks maam.. you are the best!! I am having my DWDM exam tomorrow...
What's the result?
your voice iz choooooooo chweeeeeeeet uWu
You are amazing 💖
Mam please make next playlist on scripting languages and design and analysis of algorithms and fundamentals of iot
Your explanation is very good mam but video quality is too bad please make it better so that the content can be visible clearly. For people to watch
Deep learning, cmrtc exams are from next week
Hi mam, your videos are very helpful. Thank you so much.
Please make videos on the subject Discrete Structures, it will be very helpful ❤
The other card symbol is CLOVER i guess.....and thanks for the video :✌
Do a playlist on java not for theory but for coding
Tomorrow exam...
07:46 Women.....jb direct hi cutt ho rha tha 52 then why we do fraction 😂🤣
My exam is on 22 Feb, 2023 and branch is AI&DS ,sub: probability and statistics
hello mam....ur videos are very useful to us...
i am requesting that u can make videos on WEB TECHNOLOGIES.... by 3rd jan of 2023.. clg name pscmr , vijayawada ...i am student of jntuk university..i hope u will make videos very soon as possible..thank you mam..
my respect to you ++++
Thank you for good Explanation ❤️.Can u make videos on scripting language(JNTUH)
SREE DATTHA GROUP OF INSTITUTIONS
Exam on 28th.No need full syllabus.only 2 units or 3 units
supply padaledha
Nice explanation 👌🏻
tommorow is my exam mam😂😂
You are saying is good
make videos on cryptography and network security'
if you don't mind mam please try to explain slowly I have been watching your viedos the whole day but everywhere I had felt like you are in a hurry to finish it off quickly so sometimes it is getting tough to catch your flow.
Software process project management (SPPM) e subject paina playlist cheyyandi
Mam can you explain 4,5 units.bcoz those are small units.. and take less time mam.. please complete those units.plss
Hi mam,please explain Naive Bayes algorithm using numpy pandas and matplotlib
This lectures is based on jntuh syllabus
hello ma'am ,even I'm a jntuh student ,if unit 3 is easy compared to other units then fine else could you share the videos of unit 4 & 5 if at all possible since they are comparatively smaller than unit 3 is what I felt so that we'll be able to attempt the exam with ease on 24th aug .
just for reference
UNIT - III
Bayesian learning - Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum
Likelihood and least squared error hypotheses, maximum likelihood hypotheses for predicting
probabilities, minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve
Bayes classifier, an example: learning to classify text, Bayesian belief networks, the EM algorithm.
Computational learning theory - Introduction, probably learning an approximately correct hypothesis,
sample complexity for finite hypothesis space, sample complexity for infinite hypothesis spaces, the
mistake bound model of learning.
Instance-Based Learning- Introduction, k-nearest neighbour algorithm, locally weighted regression,
radial basis functions, case-based reasoning, remarks on lazy and eager learning.
UNIT- IV
Genetic Algorithms - Motivation, Genetic algorithms, an illustrative example, hypothesis space
search, genetic programming, models of evolution and learning, parallelizing genetic algorithms.
Learning Sets of Rules - Introduction, sequential covering algorithms, learning rule sets: summary,
learning First-Order rules, learning sets of First-Order rules: FOIL, Induction as inverted deduction,
inverting resolution.
Reinforcement Learning - Introduction, the learning task, Q-learning, non-deterministic, rewards and
actions, temporal difference learning, generalizing from examples, relationship to dynamic
programming.
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.
4-2 semester .,C.S.E., jntuk university and also give some introduction about project
I Have MID Exam's On DEC 19th..
From VIT-AP
Thank you😊
make videos on the subject introduction to data science jntuh syllabus
thankyou so much ms
Thanku 👍👍👍👍👍 yar
Mam you have done mistake face/king is 12/4.but you have taken 4/4.
looking for this comment , thanx
P(F|K) means that king has already occurred and what's the probability of it being a face card, so it should be 1 as all kings are face cards.
Wow lol
thanks for the information
Those faces are of kings so it's 4/4
Conditional probability
Today is my exam mam
5/7/2023
From RVR JC college
jntuh r18 3-1 exam on (wed)
Mam make playlist on embedded Systems mam
Mam if possible... Please make videos on the subject 'discrete mathematics' according to the r15 syllabus of JNTUA 🙏🙏🙏🙏🙏
I have exams by this month end...
Mam can u please explain the occam's razor topic in unit4
Fabulous explanation...thanks
1hr before exam
I have a lil confusion bayes classifier and bayes model are same or different
Mam 4th my AI exm I am studying in North Campus delina baramulla I am form Kashmir
mam our exams are from3 june , can you make videos for design
analytics and algorithm
Hello ma'am,
My college name is Sri Krishna Institute of Technology, Bangalore.
University:- VTU
7th Semester.
Exams are from January 2023
Mam please explain compiler design of jntuh r18
can you please upload the scripting language classes
are you from data science background?
Can u tell M1 and M3
3rd Sem. MCA exam. Faridabad college of engineering and management, Haryana
thanks
Balaji institute of technology and science
Lesch Roads
mam can you share the notes of 2nd unit
Global institute of mamangement sciences bangalore
Thanks mam
We have exams from Jan 30 nd we are from JBREC jntuh college
Say important questions unit wise
ok done
YOU HAVE RETURN THE NOTES RIGHT THAT NOTES CONVERTS TO PDF AND PROVIDE IT FOR FORTHER STUDENTS 😅
IF POSSSIBLE CAN U PREPARE BEFA FOR R18 SYALLABUS (2-2 SEM) ,IT IS AT THE TIME OF 25-27 OF AUG
Bayesian learning methods video?
Madam can you explain the introduction to data science subject in data science jntuh r18 regulation
june
Ok
mam ignou mca new please create vedios
25th march 2023 Modern College
January 8 2024 vemu it 3- 1
Mam please please provide 4,5Units
mam I have exam July 26th
Anurag University
Ay did you copy your logo from T-Force RAM?
Cosm mam
My colleage name annamarchy college tirupathi give the full course mam for 1 and 2 units
dec end, inderprastha enginnering college(IPEC) (AKTU)
Gitam hyd
JNTU ANANTHAPUR
notes I need all units of ml mam
thank you akka, i write this corrector will fail me he is an assholwee
Today
Hello mam. I am pursuing masters in USA and I have my machine learning exam within 15 days. So can you please let me know the Important concepts to prepare up for my examination ?
Hindi please
I am ignou student sub code mcs224 name artificial intelligence and learning, please help me imp topics to get pass Marks. Exam date 12 aug
Flat jntuh r18
Not available
@@hrithikchinnu6910 😂