🔥 MACHINE LEARNING BCS602 IMPORTANT QUESTIONS 🔥 | VTU #sporifiilearn

追加: 2026-05-30

[00:00:01]Hi, hello everyone. Welcome back to my YouTube channel Spotify learn. Today I'm here with a video of important questions of AML, that is BCS 602 subject. Let's discuss module wise. I have separated numericals and the theory part and have given the questions. If you want the same PDF, you can comment AML and the automatic PDF will be in your DMs. And if you want the same PDF, if you're seeing in YouTube, please do DM or do comment on the reel which I post in Instagram AML or important questions. You will be getting the DM automatic DM, okay? The questions. First is about the module one. Define AML and explain the types of machine learning relationship with other fields, challenges faced in machine learning, six V's, that is volume, velocity, valid that and all you will be knowing. And the data pre-processing, applications of machine learning, supervised, unsupervised and reinforcement. Differentiate. And next is the numerical part, that is binning, min-max, Z-score, IQR, five point and kurtosis. And next is module two, concept learning.

[00:01:11]And for just if you want to design a learning system. Next is steps in designing a learning system, candidate elimination algorithm, find S with its limitations and key concept of version space and difference between candidate and find S. Next is correlation, covariance, Gauss elimination, chi square, PCA, that is principal component analysis, Z-test and SVD, that is singular value decomposition. Next is find S algorithm and candidate. These two are very important and these are also you have to be study everything.

[00:01:44]As the numerical part, we cannot predict what are the questions they may ask in the sub question. Next is difference between this linear regression, locally weighted, multiple and logistic regression, ID3. And next is decision tree learning with application advantages and disadvantages and um next is inductive bias concept of entropy and information gain KNN algorithm and this is repeated you can go through that okay. Next is numerical weighted KNN ID3 and linear regression.

[00:02:14]Next is module four prior probability base theorem brute force map MLE and MDL you have to study its full form and the explanation.

[00:02:25]Naive base and with its example perceptron and single perceptron next is simple model of artificial neuron and its structure with diagram activation function and types of activation function used in artificial neural network. Next is here only two methods are there that is naive base discrete and continuous. You can see that. Next is module five again easy grid base approach and applications of reinforcement learning challenges applications and characteristics Q learning Markov decision process four components of reinforcement learning here single linkage and K-means clustering. Do study all these questions without skipping any of these to score maximum marks. I've covered all the PYQs model question paper and what are the available resource I got I have covered almost all the concepts. If you feel any of the question is missed or anything please do let me know in the comment box and if you want the question with answer for this all all the modules that is module one to five I have prepared please do DM me on Instagram. My Instagram account is Spotify underscore learn okay.

[00:03:28]Thank you for watching the video make sure to like share comment and subscribe to my YouTube channel until next video bye-bye. I'll be uploading next video of only numericals I'll be explaining that is the questions which I've given I'll be just explaining the numericals easily and that's all for the day. Thank you for watching I'll be uploading that video also as soon as possible. Bye-bye.