Faculty Name: Yash Shah
Github Repo:
https://github.com/yash161101/Applied-AI-Batch-A
| Topic | Sub-Topics |
|---|---|
| Introduction | Introduction to the Subject, Overview, Prerequisites |
| Object Oriented Programming | Data Types/Structures, OOP Basics, Class Methods, Inheritance, Getting Familiar with Kaggle, OOPs in Data Science |
| Search | Search Basics, DFS, BFS, GBFS, A* Search, Adversarial Search, Minimax, Alpa-Beta Pruning |
| Knowledge | Propositional Logic, Logical Connections, Entailment, Inference, Model Checking, Resolution, First Order Logic |
| Uncertainty | Probability, Conditional Probability, Bayes’ Rule, Joint Probability, Bayesian Networks, Sampling, Markov Models, Hidden Markov Models |
| Optimisation | Local Search, Hill Climbing, Simulated Annealing, Linear Programming, Constraint Satisfaction, Backtracking Search |
| Learning | Basics of ML, Supervised Learning, Classification, kNN, Linear Regression, perceptron learning rule, SVMs, Decision Trees, Overfitting, Under fitting, Evaluation Metrics, Regularisation, Cross Validation, Regression, Maths behind Linear Regression, Correlation vs Causation, OLS Estimation, Assumptions of Linear Regression, Multivariable Regression,Stepwise Regression and Feature Selection, Bagging and Boosting Algorithms, Random Forrest, XGBoost, Dimensionality reduction with PCA, Gradio, Reinforcement Learning (Intro), K-means Clustering |
| Neural Networks | Artificial Neural Networks. Activation Functions. Gradient Descent. Backpropagation. Overfitting. |