Coursework

Planning Representations and Graph Construction (Grid, Lattice, Skeletonization, etc.)

Grid-based Planning and Search Algorithms (A*, Multi-goal A*, Weighted A*, Backward A*, Anytime and Incremental A*)

Sampling-based Planning (PRM, RRT, RRT Connect, RRT* and RRT* with sampling heuristics)

Symbolic Planning

Filtering, Gaussian Filters, Laplacian Pyramid

Corner Detection, Feature Descriptors

CNNs

Homography Estimation

Camera Projection and Models

Multi-view Geometry (Pose Estimation, Triangulation, Reconstruction)

Stereo Matching

Optical Flow

Structure from Motion

Perceptron Model, Activation Functions, Optimizers, Loss Functions, Backpropagation

Fully Connected Neural Networks (Audio Classification)

CNNs (Image Classification, Verification)

RNNs (GRU, LSTM)

Attention and Transformers

Autoencoders and GANs

Graph Neural Networks

Bayesian Particle Filters

Kalman Filters for SLAM (KF, EKF, UKF)

Least Squares SLAM with Linear and Non-Linear Solvers (Pseudoinverse, LU, QR along with heuristics like COLAMD)

VLOAM , ORBSLAM

Factor Graphs and Pose Graphs

Dense SLAM (ICP, Point-based Fusion)

Occupancy Grid

Constrained and Unconstrained Convex NLP

First Order Methods : Gradient Descent, Momentum

Second Order Methods : Newton's Method, BFGS

Reduced Gradient Method, KKT Conditions

Lagrangian Method, Sequential Quadratic Programming

Penalty and Barrier Functions

Multi-objective Optimization

Subgradients

Meta-modeling

Kinematic and Dynamic Bicycle Model

Steering Mechanisms

SPI, I2C Communication

Finite State Machines

Arduino 

DFM, DFA