AL-702 (D) · Machine Learning for Data Science/Official Syllabus
Machine Learning for Data Science (AL-702 (D)) - Official Syllabus
1
Unit 1
Introduction
- Algorithms and Machine Learning
- Introduction to algorithms
- Tools to analyze algorithms
- Algorithmic techniques: Divide and Conquer, examples
- Randomization
- Applications
2
Unit 2
Algorithms
- Graphs, maps, Map searching
- Application of algorithms: stable marriages example
- Dictionaries and hashing
- search trees
- Dynamic programming
3
Unit 3
Application to Personal Genomics
- Linear Programming
- NP completeness
- Introduction to personal Genomics
- Massive Raw data in Genomics
- Data science on Personal Genomes
- Interconnectedness on Personal Genomes
- Case studies
4
Unit 4
Machine Learning
- Introduction
- Classification
- Linear Classification
- Ensemble Classifiers
- Model Selection
- Cross Validation, Holdout
5
Unit 5
Machine Learning Applications
- Probabilistic modelling
- Topic modelling
- Probabilistic Inference
- Application: prediction of preterm birth
- Data description and preparation
- Relationship between machine learning and statistics
Reference Books
- Introduction to Machine Learning
- Machine Learning
- Data Science and Machine Learning: Mathematical and Statistical Methods Machine Learning & Pattern Recognition
- Hands-On Data Science and Python Machine Learning
- Machine Learning in Data Science Using Python
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