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.