Machine learning
Course homepage: https://sites.google.com/site/shouvikchakrabortycse/teaching/machine-learning
Downloads: Python Latest Version
Simple K-Means Clustering (on Synthetic Data)
Reference books:
1. Mathematics for Machine Learning by Deisenroth, Faisal, and Ong
2. Understanding Machine Learning: From Theory to Algorithms by Shalev-Shwartz and Ben-David
3. Introduction to Machine Learning by Nilsson
4. Introduction to Machine Learning by Smola and Vishwanathan
5. Machine Learning by Mitchell
Reference books (For additional knowledge):
1. Artificial Intelligence A Modern Approach by Russel and Norvig
2. Genetic Algorithm by Goldberg
3. Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow by Géron
4. Neural Networks for Pattern Recognition by Bishop
5. Pattern Recognition and Machine Learning by Bishop
Reference books (For Python):
2. Python Pocket Reference by Lutz
3. Python Cookbook by Beazley and Jones
4. Mastering Machine Learning with Python in Six Steps by Swamynathan
5. Learn Python in One Day and Learn it Well by Chan
Course Lectures (Theory):
[pdf] Introduction to Unsupervised Learning ~ Part-II (K-Means) [ppsx] Download
[pdf] Introduction to Unsupervised Learning ~ Part-III (PCA Introduction)
[pdf] Introduction to Unsupervised Learning ~ Part-IV (PCA Math) [ppsx] Download
[pdf] Introduction to Unsupervised Learning ~ Part-VI (Matrix Factorization and Completion)
Questions of internal Examinations: