【A-level】EEE工程系，到底是在學什么?

工程，作為一個應用性極強的專業，

滲透到了生活的方方面面。

工程系的學生，

未來可從事的職業也非常寬廣。

想不想了解一下工程系在學什么呢？

接著往下看吧！

數學

無論哪種工程系，都離不開數學的學習。但工程師們更多把數學作為工具使用，對于數學的學習也是更偏向應用而非理論。工程系通常需要學習的數學知識有：

1.?Calculus?I

這是微積分相關知識，學習的時候會由淺入深。涵蓋的topic會有：

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.

總之，工程系的數學學習主要包括微積分，代數，線性代數及統計。英國的本科大一通常會減緩學習進度以確保把每位學生拉倒同一學術水平線。數學的學習也是如此，在大一，數學的學習更多是復習和稍微拓展A-level高數的內容。

所以有意加入工程大家庭的小伙伴們，要留心高數的學習哦~ 這樣大一或許可以有更多時間享受生活呢（撒網捕魚）！

Electrical & Electronic Engeering

電子電器工程通常集合了硬件及軟件的學習，一名好的電子電器工程師不僅會編程還會搭電路制作小玩意。CS的同學想來幫忙編程？對不起，這個我會。Design Engineering的同學要幫忙設計？對不起，這個我早算好應該元件數值了。（說白了：我自己就是一個軍隊）。學好了EEE可是會成為很厲害的大神哦，也許心儀的男神女神一被吸引，就接著走上人生巔峰了呢！

專業知識

1. Computer Science and Programming

這門課就是EEE學生的編程課了，不同的大學剛開始會以不同的編程語言帶學生入門。但本科下來，通常都是教會學生幾門最近市場上炙手可熱的編程語言，如: Python, C, C++, Java. 悄悄告訴你，有個軟件叫MATLAB，是一個很好的計算數學的軟件，只需要簡單的代碼，就可運算復雜的數學問題。這個軟件在工程系的應用很多，有興趣的小伙伴們可以現在開始摸索自學一下哦！平時拿它算A-level的題目也是很好的哦~比如matrix的計算，polar-coordinates的圖畫，只需幾行代碼，通通幫你搞定！

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.

電子電器工程大三時通常可以選課，小伙伴們可以根據大一大二的學習找到自己的興趣從而深入學習。通常EEE的學生會流入以下幾個分支：Power generation and supply（即能源相關學習），Communications and media（通訊系統，如光通訊），Computer systems（AI之類的高大上知識學習），Robotic systems（機器人，自動化之類的學習）。