Cloud Computing

Learning Objectives

  • To enable the student to analyse the trade-offs between deploying applications in the cloud and over the local infrastructure.
  • Compare the advantages and disadvantages of various cloud computing platforms.
  • Deploy applications over commercial cloud computing infrastructures such as Amazon Web Services, Windows Azure, and Google App Engine.
  • Program data intensive parallel applications in the cloud.

About the Course

Cloud Computing is the on-demand course for the industry, the course teaches storing and retrieving data globally. Softlogic Systems provides the best practice on cloud computing usage to handle big data of an organization with the remote server access. We offer preeminent placement assistance and worthwhile certification after the successful course completion along with adequate hands-on experiences based on our industry-relevant cloud computing course syllabus to perform well in the companies from the beginning. 

Program Objectives

  • The course presents a top-down view of cloud computing, from applications and administration to programming and infrastructure.
  • Its main focus is on parallel programming techniques for cloud computing and large-scale distributed systems which form the cloud infrastructure.
  • Overview of cloud computing, cloud systems, Cloud Service Administration, Accessing the Cloud parallel processing in the cloud, distributed storage systems, virtualization, cloud standards, and Migrating to the Cloud.
  • Knowledge about the state-of-the-art solutions for cloud computing developed by Google, Amazon, Microsoft, Yahoo, VMWare, etc. Students will also apply what they learn in one programming assignment and one project executed over Amazon Web Services.


Introduction to Artificial Intelligence, AI problems, Applications of AI, Introduction to Natural Language Processing and Expert System, Machine Learning, types of machine learning, examples. Supervised Learning: Learning class from examples, learning multiple classes, regression, model selection and generalization, Parametric Methods: Introduction, maximum likelihood estimation, evaluating estimator, Bayes’ estimator, parametric classification.


Introduction, subset selection, principal component analysis, factor analysis, multidimensional scaling, linear discriminant analysis.


Introduction, mixture densities, k-means clustering, expectation-maximization algorithm, hierarchical clustering, choosing the number of clusters. Non-parametric: introduction, non-parametric density estimation, non-parametric classification.


Introduction, training a perceptron, learning Boolean functions, multilayer perceptron, backpropagation algorithm, training procedures.


Introduction, optical separating hyperplane, v-SVM, kernel tricks, vertical kernel, defining kernel, multiclass kernel machines, one-class kernel machines.


Introduction, estimating the parameter of a distribution, Bayesian estimation, Gaussian processes.


Introduction, discrete Markov processes, hidden Markov models, basic problems of HMM, evaluation problem, finding the state sequence, learning model parameters, continuous observations, HMM with inputs, model selection with HMM. Correlation and regression: Linear regression, Rank correlation, Method of least squares Fitting of straight lines and second-degree parabola. Linear regression and correlation analysis.


Introduction, single state case, elements of reinforcement learning, temporal difference learning, generalization, partially observed state.


Introduction, single state case, elements of reinforcement learning, temporal difference learning, generalization, partially observed state.

  • Alpaydin, Introduction to Machine Learning. 2nd MIT Press, 2009.
  • P. Murphy, Machine Learning: A Probabilistic Perspective. MIT Press, 2012.
  • Harrington, Machine Learning in Action. Manning Publications, 2012
  • M. Bishop, Pattern Recognition and Machine Learning. Springer, 2011.
  • Artificial Intelligence, Elaine Rich, Kevin Knight, Shivashankar B Nair, 3rdedition, McGraw Hill, 2009