Comprehensive AI ML

Phase 1

1. Mathematical Foundations for Machine Learning (AIMLZC416)
   • Focuses on linear algebra (vectors, matrices, eigenvalues), multivariate calculus (Jacobian, Hessian), and optimization techniques like Gradient Descent and PCA.
2. Introduction to Statistical Methods (AIMLZC418)
   • Covers probability concepts, Bayes Theorem, hypothesis testing, and time series analysis (ARIMA, SARIMA).
3. Machine Learning (AIMLZG565)
   • An introduction to various kinds of learning (supervised, unsupervised), model selection, Bayesian learning, and non-linear models like Decision Trees and Support Vector Machines (SVM).
4. Deep Neural Networks (AIMLZG511)
   • Covers the approximation properties of neural networks, backpropagation, CNNs, RNNs, Transformers, and applications in time series modeling.

Phase 2

Phase 3

Phase 4 – Hands on