Aerial Robotics

Course 1/6: an introduction to the mechanics of flight and the design of quadrotor flying robots and will be able to develop dynamic models, derive controllers, and synthesize planners for operating in three dimensional environments. You will be exposed to the challenges of using noisy sensors for localization and maneuvering in complex, three-dimensional environments. Finally, you will gain insights through seeing real world examples of the possible applications and challenges for the rapidly-growing drone industry.

Computational Motion Planning

Course 2/6: This course dels with addressing the problem of how a robot decides what to do to achieve its goals. This problem is often referred to as Motion Planning and it has been formulated in various ways to model different situations. Some of the most common approaches to addressing this problem including graph-based methods, randomized planners and artificial potential fields.

Linear Algebra

Course 1/3

• • Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc.
• • Apply common vector and matrix algebra operations like dot product, inverse, and determinants.
• • Express certain types of matrix operations as linear transformations.
• • Apply concepts of eigenvalues and eigenvectors to machine learning problems.

Calculus

Course 2/3

• • Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients.
• • Approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods.
• • Visually interpret differentiation of different types of functions commonly used in machine learning.
• • Perform gradient descent in neural networks with different activation and cost functions.

Probability & Statistics

Course 3/3

• • Describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions.
• • Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science like Bernoulli, Binomial, and Gaussian distributions.
• • Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems.
• • Assess the performance of machine learning models using interval estimates and margin of errors.
• • Apply concepts of statistical hypothesis testing to commonly used tests in data science like AB testing.