#45 Bayesian Belief Networks - DAG & CPT With Example |ML|

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Trouble- Free

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The explanation was so simple and clear to understand ! Hatsoff to your great work, I'm having my exam in 6 hours and my anxiety is freed due to you ma'am !

uchihamadara

A patient visits a doctor suspecting that he may have lung cancer after suffering short of breath. The doctor knows that besides lung cancer the patient might be suffering from tuberculosis. He also notes that smoking and exposure to pollution are key causes of both diseases, but taking an X-ray would indicate that the patient has cancer or tuberculosis. Given that the P (patient is a smoker) =0.2 and P (patient was exposed to low pollution)=0.8, Using the Bayesian networks following all steps determine the accuracy of the output from the X-ray results.

Junienyaru

Thank you mam this topic is very frustrated for me and you clear my doubts in just 15 min 🙏🙏🙏🙏

Haven_Hue

Amazing lecs . I'm effortlessly clear with concepts nw

Forever._.curious..

Ur explanation is awesome. First time i am listening

sridharmakkapati

Mam Please complete 5th chapter as per jntuh syllabus, we have exam on Tuesday

kavyasri

Complete 3 units mam and try to complete 4 or 5 for the safe side....
Thank you mam

pavankalyan

Given a discrete 𝐾-class dataset containing 𝑁 points, where sample points are described using 𝐷 features with each feature capable of taking 𝑉 values, how many parameters need to be estimated for Naïve Bayes Classifier?

a) V^D K
b) K^V^D
c) VDK
d) K(V+D)

please answer to this gate sample question mam.

pooraniayswariya

Can u explain these topics
Advanced Knowledge Representation and Reasoning: Knowledge Representation Issues, Non- monotonic Reasoning,
Other Knowledge Representation Schemes.
Reasoning Under Uncertainty: Basic probability, Acting Under Uncertainty, Bayes’ Rule, Representing
Knowledge in an Uncertain Domain, Bayesian Networks.

maheshbabupolanki

Great explanation! However, going by the logic for calculating only parent probability, we also need calculations for bark & hide. Just a thought.

bingochipspass

i think p(A/B, E) instead of p(A/^B, ^E) in example if i am wrong please mention why so mam!

kumbhanagarjuna

Can you show how did you come up with probability of 9, 3, 18 & 18?

Saisharath

I have exam at 24th august machine learning jntuh syllabus please upload complete syllabus topics

katkamshravani

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.

govardhanreddy

Above mentioned examples is enough to wrote in exams

prakashp

Hi Trouble free, all your videos are simple and clear, I have exam in next month so please explain Gradient Ascent Training of Basian Network, pls

vindyasemith

Ur explained very well mam Tqu so much

Anand____

Hi! How do you took the values for that dog bark and alla continue problems.

c.meghana

Please complete 3rd unit atleast by Tomorrow mam
We have exam on 24th.
So that atleast we'll pass

kondangantishruthiigoud

maximum likehood for predicting probabilites please explain

varshithamunaganuri

#45 Bayesian Belief Networks - DAG & CPT With Example |ML|

#45 Bayesian Belief Networks - DAG & CPT With Example |ML|

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