【A-level】EEE工程系，到底是在学什么?

工程，作为一个应用性极强的专业，

渗透到了生活的方方面面。

工程系的学生，

未来可从事的职业也非常宽广。

想不想了解一下工程系在学什么呢？

接着往下看吧！

数学

无论哪种工程系，都离不开数学的学习。但工程师们更多把数学作为工具使用，对于数学的学习也是更偏向应用而非理论。工程系通常需要学习的数学知识有：

1. Calculus I

这是微积分相关知识，学习的时候会由浅入深。涵盖的topic会有：

Differentiation and integration of functions of one variable, with applications. Informal treatment of limits and continuity. Differentiation: definition, rules, application to graphing, rates, approximations, and extremum problems. Indefinite integration; separable first-order differential equations. Definite integral; fundamental theorem of calculus. Applications of integration to geometry and science. Elementary functions. Techniques of integration. Polar coordinates. L'Hopital's rule. Improper integrals. Infinite series: geometric, p-harmonic, simple comparison tests, power series for some elementary functions.

2. Calculus II

Calculus of several variables. Vector algebra in 3-space, determinants, matrices. Vector-valued functions of one variable, space motion. Scalar functions of several variables: partial differentiation, gradient, optimization techniques. Double integrals and line integrals in the plane; exact differentials and conservative fields; Green's theorem and applications, triple integrals, line and surface integrals in space, Divergence theorem, Stokes' theorem; applications.

3. Differential Equations

Study of differential equations, including modeling physical systems. Solution of first-order ODEs by analytical, graphical, and numerical methods. Linear ODEs with constant coefficients. Complex numbers and exponentials. Inhomogeneous equations: polynomial, sinusoidal, and exponential inputs. Oscillations, damping, resonance. Fourier series. Matrices, eigenvalues, eigenvectors, diagonalization. First order linear systems: normal modes, matrix exponentials, variation of parameters. Heat equation, wave equation. Nonlinear autonomous systems: critical point analysis, phase plane diagrams.

4. Complex Variables with Applications

Complex algebra and functions; analyticity; contour integration, Cauchy's theorem; singularities, Taylor and Laurent series; residues, evaluation of integrals; multivalued functions, potential theory in two dimensions; Fourier analysis, Laplace transforms, and partial differential equations.

5. Probability and Statistics

Elementary introduction with applications. Basic probability models. Combinatorics. Random variables. Discrete and continuous probability distributions. Statistical estimation and testing. Confidence intervals. Introduction to linear regression.

6. Linear Algebra

Basic subject on matrix theory and linear algebra, emphasizing topics useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, singular value decomposition, and positive definite matrices. Applications to least-squares approximations, stability of differential equations, networks, Fourier transforms, and Markov processes. Uses linear algebra software.

总之，工程系的数学学习主要包括微积分，代数，线性代数及统计。英国的本科大一通常会减缓学习进度以确保把每位学生拉倒同一学术水平线。数学的学习也是如此，在大一，数学的学习更多是复习和稍微拓展A-level高数的内容。

所以有意加入工程大家庭的小伙伴们，要留心高数的学习哦~ 这样大一或许可以有更多时间享受生活呢（撒网捕鱼）！

Electrical & Electronic Engeering

电子电器工程通常集合了硬件及软件的学习，一名好的电子电器工程师不仅会编程还会搭电路制作小玩意。CS的同学想来帮忙编程？对不起，这个我会。Design Engineering的同学要帮忙设计？对不起，这个我早算好应该元件数值了。（说白了：我自己就是一个军队）。学好了EEE可是会成为很厉害的大神哦，也许心仪的男神女神一被吸引，就接着走上人生巅峰了呢！

专业知识

1. Computer Science and Programming

这门课就是EEE学生的编程课了，不同的大学刚开始会以不同的编程语言带学生入门。但本科下来，通常都是教会学生几门最近市场上炙手可热的编程语言，如: Python, C, C++, Java. 悄悄告诉你，有个软件叫MATLAB，是一个很好的计算数学的软件，只需要简单的代码，就可运算复杂的数学问题。这个软件在工程系的应用很多，有兴趣的小伙伴们可以现在开始摸索自学一下哦！平时拿它算A-level的题目也是很好的哦~比如matrix的计算，polar-coordinates的图画，只需几行代码，通通帮你搞定！

2. Circuits and Electronics

Fundamentals of the lumped circuit abstraction. Resistive elements and networks, independent and dependent sources, switches and MOS devices, digital abstraction, amplifiers, and energy storage elements. Dynamics of first- and second-order networks; design in the time and frequency domains; analog and digital circuits and applications. Design exercises. Occasional laboratory.

3. Signals and Systems

 Presents the fundamentals of signal and system analysis. Topics include discrete-time and continuous-time signals, Fourier series and transforms, Laplace and Z transforms, and analysis of linear, time-invariant systems. Applications drawn broadly from engineering and physics, including audio and image processing, communications, and automatic control.

4. Computation Structures

Introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies. Multilevel implementation strategies; definition of new primitives (e.g., gates, instructions, procedures, and processes) and their mechanization using lower-level elements. Analysis of potential concurrency; precedence constraints and performance measures; pipelined and multidimensional systems. Instruction set design issues; architectural support for contemporary software structures.

5. Introduction to Algorithms

Introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. Emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

6. Introduction to Inference

Introduces probabilistic modeling for problems of inference and machine learning from data, emphasizing analytical and computational aspects. Distributions, marginalization, conditioning, and structure; graphical representations. Belief propagation, decision-making, classification, estimation, and prediction. Sampling methods and analysis. Introduces asymptotic analysis and information measures. Computational laboratory component explores the concepts introduced in class in the context contemporary applications. Students design inference algorithms, investigate their behavior on real data, and discuss experimental results.

7. Signals, Systems and Inference

Covers signals, systems and inference in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; and group delay. State feedback and observers. Probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization. Least-mean square error estimation; Wiener filtering. Hypothesis testing; detection; matched filters.

8. Nanoelectronics and Computing Systems

Studies interaction between materials, semiconductor physics, electronic devices, and computing systems. Develops intuition of how transistors operate. Topics range from introductory semiconductor physics to modern state-of-the-art nano-scale devices. Considers how innovations in devices have driven historical progress in computing, and explores ideas for further improvements in devices and computing. Students apply material to understand how building improved computing systems requires knowledge of devices, and how making the correct device requires knowledge of computing systems. Includes a design project for practical application of concepts, and labs for experience building silicon transistors and devices.

电子电器工程大三时通常可以选课，小伙伴们可以根据大一大二的学习找到自己的兴趣从而深入学习。通常EEE的学生会流入以下几个分支：Power generation and supply（即能源相关学习），Communications and media（通讯系统，如光通讯），Computer systems（AI之类的高大上知识学习），Robotic systems（机器人，自动化之类的学习）。