Artificial Intelligence and Machine Learning

A repo for tasks, assignments and everything Artificial Intelligence and Machine Learning

The 1st digit after the type of resource is the year. (eg: Class Exercise 3.3 would stand for a class exercise I did in 3rd year, and it is the 3rd class exercise)

Labs

• Gym Lab: A lab that familiarizes the Gym module.

• Lab 3.1: BFS and DFS Traversal of a graph im Python.

• Lab 3.2: Implementing Uniform Cost Search and Deepening Depth-First Search on a given graph

• Lab 3.3: Implementing Natural Language Processing to classify text

• Lab 4.1: Implementing Gradient Descent on random samples of data and observing MSE, Bias and Slope

• Lab 4.2: Feature Selection using Lasso and Ridge Regression

• Lab 4.3: Implementing Q-Learning

• Lab 4.5: Implementing a Neural Network to Regress data.

Class Exercises

• Class Exercise 3.1: Classification and Performance Matrix

• Class Exercise 3.2: Natural Language Processing

• Class Exercise 3.3: Vector Comparison using TF-ID

• Class Exercise 4.1: Difference between Classical and Statistical Methods in Machine Learning. [Logical Regression vs Decision Trees]

• Class Exercise 4.2: Investigating the effect that outliers have on Generative Models vs Discriminative Models.

• Class Exercise 4.3: Linear Regression and Gradient Descent

• Class Exercise 4.4: Ridge Regression

• Class Exercise 4.5: Decision Trees, Pruning and Random Forest

• Class Exercise 4.6: Sentence Similarity using KMeans

• Class Exercise 4.7: Gym Agent Simulations

• Class Exercise 4.8: ReLu and Convolution Layers for Image Classificaition

• Class Exercise 4.9: False news detection using a Neural Network and NLP

• Class Exercise 4.10: Text Classification using a Neural Network and NLP as well as Model Deployment

Make Up Lab

• Language Identification using Naive Bayes