Advances in Data Science

Unit I

Deep Learning and Neural Networks: Introduction to deep learning, Neural network architectures: CNNs, RNNs, and Transformers, Training deep learning models, Transfer learning and fine-tuning, Advanced topics in deep learning: Generative models, GANs, and reinforcement learning.

Unit II

Natural Language Processing (NLP): Basics of NLP: Tokenization, POS tagging, and parsing Text classification and sentiment analysis, Named Entity Recognition (NER) and entity linking, Word embedding’s and language modeling, Neural machine translation and language generation.

Unit III

Big Data Analytics: Introduction to big data analytics, Distributed computing and storage: Hadoop and Spark, Processing big data: MapReduce and Spark programming, Machine learning on big data: Scalable algorithms and frameworks, Stream processing and real-time analytics

Unit IV

Explainable AI and Ethical Considerations: Interpretable machine learning models, Model explainability and feature importance, Bias, fairness, and accountability in AI, Privacy and security in data science, Ethical guidelines and responsible AI practices

Textbooks:

1. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
2. "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper
3. "Big Data: A Revolution That Will Transform How We Live, Work, and Think" by Viktor Mayer-Schönberger and Kenneth Cukier

Reference Books:

1. "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable" by Christoph Molnar
2. "Fairness and Machine Learning: Limitations and Opportunities" edited by Solon Barocas, Moritz Hardt, and Arvind Narayanan
3. "Privacy and Big Data: The Players, Regulators, and Stakeholders" by Terence Craig and Mary Ludloff