Poonam Phate

Technical Specialties:- - Python, Java, C, WEKA, Scikit Learn 1. Machine Learning Project 1: Supervised Learning Applied supervised Machine learning algorithms (Decision Trees, Artificial Neural Networks, Boosting, Support Vector Machines, and K-Nearest Neighbors) on Wine Quality Dataset using WEKA. The classification models designed using ML algorithms were used to classify wine samples into Good or Bad wine. Parameter tuning and cross validation techniques were applied to improve the performance of classifiers. 2. Machine Learning Project 2: Randomized Optimization Implemented randomized optimization algorithms (Randomized Hill Climbing, Simulated Annealing, Genetic Algorithm and MIMIC) on White Wine dataset in JAVA using ABAGAIL Library. The performance of these algorithms was also analyzed by applying the algorithms to Knapsack, Count ones and Four peaks optimization problems and strength and weaknesses of each algorithm was studied. 3. Machine Learning Project 3: Unsupervised Learning In this project, Unsupervised Learning Techniques Clustering (K-means clustering and Expectation Maximization) and Feature Transformation (Principle Component Analysis, Independent Component Analysis, Randomized Projection and Information Gain) were applied to Wine Dataset using WEKA. 4. Machine Learning Project 4: Markov Decision processes In this project, two Markov Decision Problems were developed(one easy and one complex problem). The optimal policy was discovered by implementing three different algorithms Value Iteration, Policy Iteration and Q-learning. 5. Artificial Intelligence Project: AI agent to Solve Ravens Progressive Matrices Developed an AI agent that could solve Ravens Progressive Matrices (RPM) using python imaging library called Pillow. The AI agent was designed using Knowledge Based Artificial Intelligence techniques Classification, Planning, Case Based Reasoning and Generate and Test.