MA60306 Big Data Analysis (3-0-0)
Lectures
Lecture 1
Lecture 2
Lecture 3
Lecture 4
Lecture 5
Lecture 6
Lecture 7
Lecture 8
Lecture 9
Lecture 10
Lecture 11
Lecture 12
Lecture 13
Lecture 14
Lecture 15
Lecture 16
Lecture 17
Lecture 18
Lecture 19
Lecture 20
Lecture 21
Lecture 22
Lecture 23
Lecture 24
Lecture 25
Network slides pages: 2-26, 34-95, 101-117, 128-140
Assignments: Sent through email
Viva
Schedule
Reference Books:
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning . Springer.
Strang, G. (2019). Linear Algebra and Learning from Data. Cambridge: Wellesley- Cambridge Press.
Blum, A., Hopcroft, J., & Kannan, R. (2020). Foundations of Data Science. Cambridge University Press.
Higham, N.J., (2002). Accuracy and Stability of Numerical Algorithms. SIAM.
Wang, J. (2012). Geometric Structure of High-dimensional Data and Dimensionality Reduction. Springer.
Izenman, A.J., (2008). Modern Multivariate Statistical Techniques. Springer.
Bishop, C. M., (2006). Pattern Recognition and Machine Learning. Springer.
Simeone O., (2018). A Brief Introduction to Machine Learning for Engineers
Simeone O., (2022). Machine Learning for Engineers, Cambridge University Press.
Murphy, K.P., (2012). Machine Learning: A Probabilistic Perspective, MIT Press.
Bremaud, P. (1999). Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues, Springer.