Post Graduate Programme in Artificial Intelligence and Machine Learning

FEATURE ENGINEERING (4 WEEKS)

Feature Engineering is an important step to develop and improve performance of Machine Learning models. In this course, students will learn different data wrangling techniques that help transforming the raw data to an appropriate form on which learning algorithms can be applied.

This course enables students to identify and implement appropriate feature extraction and pre-processing techniques. The Visualization techniques will also be taught in this course.

CLASSIFICATION (9 WEEKS)

The course on Classification lays down a strong foundation on the algorithmic perspective of popular classification algorithms - k-NN, Naïve Bayes, Decision Tree, Logistic Regression and SVM. The implementation details of these models, along with tuning of parameters will be illustrated. The course also covers concepts such as ensemble methods like bagging, boosting, Random Forest, and interpretability of the predicted models.

UNSUPERVISED LEARNING & ASSOCIATION RULE MINING (7 WEEKS)

The course on Unsupervised Learning & Association Rule Mining focuses on finding natural groups or clusters that are present in the data. The course will cover lustering algorithms like K-means, Hierarchical & DBSCAN algorithms, Hidden Markov Models for time series prediction, and market basket analysis to generate the interesting rules from a transactional database.

Text Mining (5 weeks)

Text mining is the process of deriving high-quality information from text and this is the fifth course of the program. This course aims to equip students with adequate knowledge in extracting the relevant text data and skills to identify patterns therein. This course covers topics like converting documents to vectors, parts of speech tagging, topic modelling, sentiment analysis and recommender systems.

• Document vectorization and Parts of Speech Tagging
• Introduction to Part of speech tagging, Part of speech tagging using HMM-1, Implementing POS Tagging in Python
• Topic modelling using LDA
• Introduction to Sentiment Analysis
• Recommender Systems

Deep Learning and ANN (6 weeks)

Deep learning is an evolving subfield of Machine Learning and this course starts with traditional Neural Networks followed by sequential networks, Convolution Networks, Autoencoders and Generative deep learning models. The implementation details of these deep learning models along with tuning of the parameters will be illustrated in this course.

• Artificial Neural Network
• Sequence Modeling in Neural Network
• Deep Learning
• Convolution Networks with Deep Learning
• Autoencoders with Deep Learning
• Generative deep learning models

CAPSTONE PROJECT (8 WEEKS)

During the 8-week Capstone Project, participants will work in teams to design and solve a real-world business problem encompassing data science pipeline using AI&ML techniques under the mentorship of BITS Pilani faculty members and senior Industry practitioners.