This course covers preliminaries of deep learning required for under graduate students. In this course, we will start with mathematical pre-requisites for deep learning and covers 3 different frameworks.  The following content will be covered in the course:

UNIT-I: Introduction to Machine Learning and Preparing to Model

• Introduction to Machine Learning - Introduction, Types of Machine Learning, Applications of Machine Learning, Issues in Machine Learning. 

• Preparing to Model- Introduction, Machine Learning Activities, Basic Types of Data in Machine Learning, Exploring Structure of Data, Data Quality and Remediation, Data Pre-Processing

UNIT-II: Modelling & Evaluation, Basics of Feature Engineering

• Modelling & Evaluation- Introduction, selecting a Model, training a Model (for Supervised Learning), Model Representation and Interpretability, Evaluating Performance of a Model.

• Basics of Feature Engineering- Introduction, Feature Transformation – Feature Construction, Feature Extraction, Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Linear Discriminant Analysis (LDA), Feature Subset Selection

UNIT-III: Regression

• Introduction to regression analysis, Simple linear regression, Multiple linear regression, Assumptions in Regression Analysis, Main Problems in Regression Analysis, Improving Accuracy of the linear regression model, Polynomial Regression Model, Logistic Regression, Regularization, Regularized Linear Regression, Regularized Logistic Regression.

UNIT-IV: Supervised Learning: Classification

• Classification- Introduction, Example of Supervised Learning, Classification Model, Classification Learning Steps.

• Common Classification Algorithms - k-Nearest Neighbour (kNN), Support vector Machines (SVM), Random Forest model.

UNIT-V: Other Types of Learning

• Ensemble Learning- Bagging, Boosting, Stacking and its impact on bias and variance, AdaBoost,Gradient Boosting Machines, XGBoost.

• Reinforcement Learning - Introduction, Q Learning.