# Recent questions tagged machine-learning

1
why should working professional prefer GATE over trending technology like machine learning. If they spent same amount of time to crack GATE in machine learning they would be getting better results. can some one explain me with pros and cons of preparing for GATE and getting started with Machine learning course ?
2
I am a dropper and have worked an year on machine learning in the industry. I want to do learn more about ML/AI and image processing. I am getting a rank around 200(from GO rank predictor). I am open to 2 or 3 year courses and any college is fine as long as I get to work in my field of interest and the resources are good there. What are the best options for me?
3
Consider the following data. Data describes that a person should/should not buy a house with a given specification. Size (feet2 ) No. of Bed-Rooms Age of Home (Years) Buy 2104 5 25 No 1416 3 30 Yes 1350 4 15 Yes 750 2 05 No Use Gradient Descent ... above classification problem in any language. (c) Run the algorithm for 30 iterations or if it is converged earlier. (d) Display the Error Surface
1 vote
4
Recently, IITH has introduced machine learning as a choice beside general CS. As its newly introduced is it better to give it a first priority than General CS if someone is interested in machine learning? Any comments on this would be appreciated.
1 vote
5
Can anyone tell me their interview experience for machine learning and computing at IIST ?? i heard there are only 3 seats for general category ? what is the procedure for entrance into the course and on what topics will we be tested ?
6
What is the step by step procedure to apply algorithms like Decision Tree, Naive Bayes, etc... on a dataset using Python?
1 vote
7
In which situation generative model performs better than discriminative?
8
9
Hello everyone, I want to ask some questions related to data science and GATE. First, how to launch career in Data science field? What is role of GATE score in this career? Second, Which college/university has good data science / machine learning course? Suggest ... with detailed insights. Thanks for reading such post. Please do answer and help someone like me. Thanks in advance for your answer.
10
What is the scope of Machine Learning in India from the prospect of a good job? Is IITK or IITD a good choice to do M. Tech to start a career in this field? I have started doing machine learning courses and have developed a keen interest in it. I want to know the future prospects if I pursue my M. Tech from one of the IITs with goal of learning Machine Learning. Thank You!
11
Is getting into iit's necessary to become good at machine learning and data science or there are other ways to make a career in it ? If iit is necessary then what is cutoff for selecting this stream. if someone could write a post on this topic or answer in comments then it will be very helpful for me in planning accordingly.
12
Match the following learning modes $w.r.t$. characteristics of available information for learning : a. Supervised i. Instructive information on desired responses, explicitly specified by a teacher. b. Recording ii. A priori design information for memory storing c. Reinforcement iii. Partial information ... about desired responses Codes : a b c d i ii iii iv i iii ii iv ii iv iii i ii iii iv i
13
An artificial neuron receives n inputs $x_1, x_2, \dots , x_n$ with weights $w_1, w_2, \dots , w_n$ attached to the input links. The weighted sum ____ is computed to be passed on to a non-linear filter $\phi$ called activation function to release the output. $\Sigma \: w_i$ $\Sigma \: x_i$ $\Sigma \: w_i + \Sigma \: x_i$ $\Sigma \: w_i \cdot \Sigma \: x_i$
14
In a single perceptron, the updation rule of weight vector is given by $w(n+1) = w(n) + \eta [d(n)-y(n)]$ $w(n+1) = w(n) - \eta [d(n)-y(n)]$ $w(n+1) = w(n) + \eta [d(n)-y(n)]*x(n)$ $w(n+1) = w(n) - \eta [d(n)-y(n)]*x(n)$
1 vote
15
The decision tree classifier is a widely used technique for Classification Association Partition Clustering
1 vote
16
Match the following: a. Supervised learning 1. The decision system receives rewards for its action at the end of a sequence of steps b. Unsupervised learning 2. Manual labels of inputs are not used c. Re-inforcement learning 3. Manual labels of inputs are used d. Inductive learning 4. System learns by example a b c d A 1 2 3 4 B 2 3 1 4 C 3 2 4 1 D 3 2 1 4
17
Back propagation is a learning technique that adjusts weights in the neutral network by propagating weight changes. Forward from source to sink Backward from sink to source Forward from source to hidden nodes Backward from sink to hidden nodes
18
In Delta Rule for error minimization weights are adjusted w.r.to change in the output weights are adjusted w.r.to difference between desired output and actual output weights are adjusted w.r.to difference between output and output none of the above
Perceptron learning, Delta learning and $LMS$ learning are learning methods which falls under the category of Error correction learning - learning with a teacher Reinforcement learning - learning with a critic Hebbian learning Competitive learning - learning without a teacher