1.Remarks on explanation-based learning 2.Explanation-Based learning of search control knowledge ANALYTICAL LEARNING-2 1.Using prior knowledge to alter the search objective. 2.Using prior knowledge to argument search operators. 3.Convincing Inductive and Analytical Learning 4.Motivation 5.Inductive-Analytical approaches to learning 6.Using prior knowledge to initialize the hypothesis. please explain these topics mam....our exams are in this month (August) only....
@-HLSriAditya3 сағат бұрын
I LOVE YOU
@narendarmallireddy97324 сағат бұрын
continue mam
@Tobi_7944 сағат бұрын
It took me 30 mins to understand this .. literally am scolding the subject creators like why we need all this stuff , especially for a cse student ...bullshit
@pavan27348 сағат бұрын
did you took crossover points randomly????
@md.fareenaafshamd.fareenaa733413 сағат бұрын
Cloud computing
@marvelfan630914 сағат бұрын
Time complexity???
@taehungv762022 сағат бұрын
Mam, please do videos on nlp(natural language processing) please mam please
@vedantvs9220Күн бұрын
Thank you so much mam
@AnishKandukuriКүн бұрын
Tyy
@rameshpiumika4596Күн бұрын
Good job.. Thank you so much😊 Valuable explanation..more theoretical parts are same our degree program.. I am undergraduate of sri lankan university
@ayman2690Күн бұрын
hi can u pls make machine learning for vtu students studying BE our syllabus pdf is available on vtu website . u need to go to UG scheme engg 2021 scheme and select BE in aiml and under that 6 th sem syllabus
@chandramahi9479Күн бұрын
Thank you for great explanation 🙏
@btechmathematics2926Күн бұрын
please don't teach without verify the topic properly. Parallelizing a genetic algorithm doesn't mean using multiple algorithms to solve a task. Instead, it means dividing the work of a single genetic algorithm across multiple processors or cores to speed up the computation process.
@prasannapabba3528Күн бұрын
august 29th vnr vjiert engg college
@Punyashree-ze3pyКүн бұрын
Please make videos on computer organisation and neural network
@LuckyLucky-dr1ceКүн бұрын
Great explanation
@boddepallikalyan2170Күн бұрын
super
@MekalaBadhrinathReddyКүн бұрын
Congrats akka🎉, telugu😍
@CMSADGURUSAIКүн бұрын
notes pdf will helpful for good learning
@India_Shorts-r8m2 күн бұрын
PDF
@rakeshc4442 күн бұрын
Me telugu super lv it
@pudurukoushik68652 күн бұрын
mi explanation is good but you've been doing so fast like very fast explanation some people can't understand that much try tell medium level please 😂🎉😊
@mkbot992 күн бұрын
Tomorrow ml exam 😂
@awanishmishra97822 күн бұрын
thanks mam
@turbofas74783 күн бұрын
0:55
@anjalipendem64943 күн бұрын
says i dont know the subject myself and then proceeds to give a nice explanation... XD thanks bruhhh
@polavarapudurgesh39373 күн бұрын
for single layer only it is hard but multilayer hush...
@turbofas74783 күн бұрын
1:02
@chinmay99753 күн бұрын
I am really Trouble Free, Thank You.
@chinmay99753 күн бұрын
I am really Trouble Free, Thank You
@user-mw2nx5pf6n3 күн бұрын
Exam is on aug 2/2024
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
@govardhanreddy18393 күн бұрын
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.
Пікірлер
U made my preparation much easy ..tq lov u❤
Mam do some videos about PLC.....
Super explanation 😊 easy to understand
1.Remarks on explanation-based learning 2.Explanation-Based learning of search control knowledge ANALYTICAL LEARNING-2 1.Using prior knowledge to alter the search objective. 2.Using prior knowledge to argument search operators. 3.Convincing Inductive and Analytical Learning 4.Motivation 5.Inductive-Analytical approaches to learning 6.Using prior knowledge to initialize the hypothesis. please explain these topics mam....our exams are in this month (August) only....
I LOVE YOU
continue mam
It took me 30 mins to understand this .. literally am scolding the subject creators like why we need all this stuff , especially for a cse student ...bullshit
did you took crossover points randomly????
Cloud computing
Time complexity???
Mam, please do videos on nlp(natural language processing) please mam please
Thank you so much mam
Tyy
Good job.. Thank you so much😊 Valuable explanation..more theoretical parts are same our degree program.. I am undergraduate of sri lankan university
hi can u pls make machine learning for vtu students studying BE our syllabus pdf is available on vtu website . u need to go to UG scheme engg 2021 scheme and select BE in aiml and under that 6 th sem syllabus
Thank you for great explanation 🙏
please don't teach without verify the topic properly. Parallelizing a genetic algorithm doesn't mean using multiple algorithms to solve a task. Instead, it means dividing the work of a single genetic algorithm across multiple processors or cores to speed up the computation process.
august 29th vnr vjiert engg college
Please make videos on computer organisation and neural network
Great explanation
super
Congrats akka🎉, telugu😍
notes pdf will helpful for good learning
PDF
Me telugu super lv it
mi explanation is good but you've been doing so fast like very fast explanation some people can't understand that much try tell medium level please 😂🎉😊
Tomorrow ml exam 😂
thanks mam
0:55
says i dont know the subject myself and then proceeds to give a nice explanation... XD thanks bruhhh
for single layer only it is hard but multilayer hush...
1:02
I am really Trouble Free, Thank You.
I am really Trouble Free, Thank You
Exam is on aug 2/2024
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.
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.
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.
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- 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- 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- 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- 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- 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- 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- 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- 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 - 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 - 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 - 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.