# Engineering Master’s Prep (EMP) Program

### GET A COMPETITIVE EDGE TO ELITE GRADUATE SCHOOLS.

##### Students in EMP will:
• Learn from award-winning, industry-leading professors
• Review core engineering concepts and prepare for success in graduate school programs
• Study at a University of California, Berkeley School of Engineering program in your own country
• Earn a Certificate of Achievement and a Referral Letter from the Director of the EMP program at Berkeley College of Engineering

### Tarek Zohdi

#### Program Overview

##### This requires:
• 6–8 instructional hours via Global Live technology
• 12-20 independent study hours
##### When you finish the program, you will receive:
• Berkeley College of Engineering Certificate of Achievement
• Referral letter signed by the Academic Program Director

#### Module 1: Programming and Physics

##### THE ESSENTIALS OF PROGRAMMING

Basic arrays, definition and manipulations, functions and plotting—essential computer programming basics for those interested in computer science, data science, and information management.

• Basic programming
• Basic arrays, definitions, and manipulations
• Functions and plotting
##### FUNDAMENTAL PHYSICS I

Understand water, so essential to life, with the basics of fluid mechanics and thermodynamics: properties of fluids, the dynamics of fluid flows, and heat transfer.

• Basic thermodynamics
• Heat transfer
• Fluid mechanics

#### Module 2: Linear Algebra and Physics

##### THE ESSENTIALS OF LINEAR ALGEBRA

Review the essentials of linear algebra for engineering applications. Starting from matrix manipulations, extensive use of engineering applications is used to illuminate and motivate matrix operations, types of matrices, and properties of matrices.

• Motivational applications
• Matrix and vector algebra: various products
• Eigenvalues and eigenvectors
##### FUNDAMENTAL PHYSICS II

Explore how fundamental principles and concepts from mechanics are used to establish equations governing the mechanical behavior of a wide range of systems. Develop an appreciation for the analysis of the resulting equations.

• Statics
• Mechanics and materials
• Dynamics

#### Module 3: Circuits, Probability and Statistics

##### CIRCUITS

Build on the mathematical tools taught earlier in the program: how analog circuits can be modeled as systems of algebraic and differential equations, the basics of transistor modeling, and how transistors can be used to build digital circuits.

• Basic principles of resistive circuits
• Translation of resistive circuits into systems of linear algebraic equations
• Basic principles of inductive and capacitive circuits
• First order and second order differential equation circuits
##### THE ESSENTIALS OF PROBABILITY AND STATISTICS

Review the foundations of probability and statistics, covering counting problems, sample spaces, probabilities, events, and continuous and discrete random variables.

• Counting problems, sample spaces, probabilities, and events
• Continuous random variables: Gaussian and exponential distributions
• Bayesian estimation (maximum a priori and maximum likelihood)

#### Module 4: Modeling and Simulation Tools for Industrial Research Applications

##### THE FIELD OF SIMULATION MACHINE-LEARNING AND OPTIMIZATION

Learn techniques of modeling and simulation, numerical methods, and their implementation on physical problems motivated by real-world phenomena that engineers and scientists are likely to encounter in their careers involving energy systems, materials engineering, structural analysis, dynamics, controls, etc.

• Master the concept of “optimal” and Classical Gradient-based Methods
• Master modern machine-learning, evolutionary computation and genetic algorithms
##### MODELING AND SIMULATION OF AUTONOMOUS SYSTEMS-SWARMS OF UAVS

Learn about the modeling of swarm “agents”, their dynamics and numerical methods to describe their motion. Learn how to train a swarm using machine-learning optimization.

• Master the methods needed to simulate dynamics of large-scale systems
• Master application of modern optimization methods
##### MODELING AND SIMULATION OF ADVANCED MANUFACTURING 3D ROBOTIC PRINTERS

Learn how to model and simulate more complex systems, such the dynamics of a 3D printing robot, along with multiphysical electrodynamic control and machine-learning for system training/optimization.

• Apply methods of heat-transfer and dynamics simultaneously
• Master kinematics of robots

#### ENTRY REQUIREMENTS

##### This intensive program is designed for students who hope to get a master’s degree or PhD in engineering from a top-tier university. You should:
• Be entering your junior or senior year or a recent university graduate hoping to attend a post-baccalaureate engineering program
• Have a 3.0 GPA or higher
• Have an IELTS score of 6.0 or above
• Be studying engineering, computer-science, or a related major
• Be proficient in English