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IE2xx : Data Analytics, AI/ML LAB

Prerequisites topics

Programming, basic Statistics

Contents:

Students will learn various aspects related to data handling, data storage & retrieval from various sources (database, websites, etc), querying data, data cleaning, data manipulation, data summarization and data visualization.

  • Data formatting:  csv, arff, json, xml 
  • Understanding and working with time series data.
  • Understanding and Working with image data , audio and video data.
  • Processing and understanding text data, natural language data.
  • Understanding and working with graph data, maps, spatio-temporal data.

A broad list is given below

  • Linear regression
  • Classification:  logistic regression, kNN, handling class imbalance
  • Data augmentation: SMOTE 
  • Discriminant analysis: FDA, LDA
  • Clustering - Hard, soft and hierarchical, K-means
  • Dimensionality reduction, Principal component analysis, sparsity inducing regularizers
  • Understanding basic neural networks (feed-forward nets, Convolutional neural nets, recurrent neural nets) 
  • Applications of ML: Sentiment analysis, object detection, question answering, recommendation systems.

References

  • Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Pearson, Fourth Edition, 2020.
  • Richard E. Neapolitan and Xia Jiang. Artificial Intelligence with an Introduction to Machine Learning, CRC Press, Second Edition, 2018.
  • Tom Mitchell. Machine Learning, McGraw Hill Education, First edition, 2017.