Stephen's Skill Graph (Growing With My Incremental Learning)
MATH Calculus
Limits, derivatives with applications, antiderivatives, fundamental theorem of calculus, Transcendental functions, differential equations, techniques of integration, improper integrals, infinite series. Topics covered include partial derivatives; grad, div, curl and Laplacian operators; line and surface integrals; theorems of Gauss and Stokes; double and triple integrals in various coordinate systems; extrema and Taylor series for multivariate functions.
MATH Linear Algebra and Analytic Geometry
Topics include spherical and cylindrical coordinates in Euclidean 3-space, general matrix algebra, determinants, vector space concepts for Euclidean n-space (e.g. linear dependence and independence, basis, dimension, linear transformations etc.), an introduction to eigenvalues and eigenvectors.
MATH Probability theory and mathematical statistics
Introduction to the theory of probability as preparation for further study in either mathematical or applied probability and statistics. Topics include probability spaces, conditional probability, independence, random variables, distribution functions, expectation, Chebyshev's inequality, common distributions, moment-generating functions and limit theorems.
MATH Complex Function and Integral Transformation
MATH Equations of Mathematics and Physics
PKU GSM Mathematical Methods in Finance
Physics College Physics
CS Core C Programming Language
EE Elective MATLAB Language and Its Applications
EE Core Electrodynamics
Physics Quantum Mechanics
Physics Atomic Physics
Physics Thermo Dynamics and Statistic Physics
Physics Principle of Laser
PKU CFCS Introduction to Quantum Compution
This course presents an overview of the quantum computing field without assuming any prior exposure to quantum mechanics. Drawing parallels between quantum and classical computing, the course covers the physical layer briefly before moving to quantum gates, the circuit model, and quantum algorithms. Quantum information is covered through applications.
Physics Applied Optics
Physics Physics Optics
EE Core Electric Circuit
EE Core Fundamentals of Analog Electronics
EE Core Fundamentals of Digital Electronics
Theory, analysis, and design of logic circuits used in digital systems. Students will be introduced to design of switching circuits to implement logic gates, digital number representation and arithmetic circuits. They will learn how to use logic gates to construct combinational and sequential logic circuits and functional blocks. The course and the laboratory introduces the students to hardware description language and modern cad tools.
EE Core Signals and Systems
An introduction to the mathematical background in signals and systems; signal and image processing: sampling, discrete Fourier transform, filtering; linear system theory; Kalman filtering; feedback.
EE Core Digital Signal Processing
Fundamental digital signal processing. Inner product, Hilbert space, orthogonality principle, discrete-time Fourier transform, discrete Fourier transform, z-transform, multirate systems, sampling and interpolation.
EE Core Optical Instruments and Design
EE Core Optoelectronic Device and Technology
EE Core Photoelectric signals detection
EE Core The Fiber Optics and Its Applications
EE Core Nonlinear Optical Technology of Frequency Conversion and Phase Conjugation
UCB CS61C Great Ideas in Computer Architecture
The internal structure and design ideas embodied in many computers and the techniques for evaluating them. Fast arithmetic algorithms, memory system designs, pipeline techniques, input-output subsystems and parallel computing structures. Future trends in computer architecture.
CS Core Microcomputer Principle and Interface Technique
CS Core Parallel processing and architecture
CS Core Data Structure and Algorithms
Review of fundamental data structures. Analysis of algorithms: time and space complexity. Algorithm design paradigms: divide-and-conquer, exploring graphs, greedy methods, local search, dynamic programming, probabilistic algorithms, computational geometry. NP-complete problems.
CS Core Programming Methodology
Amortized and worst-case analysis of data structures. Data structuring paradigms: self-adjustment and persistence. Lists: self-adjustment with the move-to-front heuristic. Search trees: splay trees, finger search trees. Heaps: skew heaps, Fibonacci heaps. Union-find trees. Link-and-cut trees. Multidimensional data structures and dynamization.
Stanford CS224W Machine Learning with Graphs
ETH TCS Advanced Graph Algorithms and Optimization
EE Core Computer Communication Network
Introduces the basics of communications and networking. Topics include transmission media; fundamental limits; protocols and hierarchies; the OSI model; encoding of data as signals; error and flow control; medium access; routing; internetworking; transport services; high-level applications.
CS Elective Practice of Data Mining Algorithms Based On Cloud Computing Platform
CS Elective Digital Image Processing
Fundamental concepts of Computer vision and including aspects of biological vision, image formation process, image processing, feature extraction and matching, 3-D parameter estimation, applications and statistical techniques. Twelve supervised laboratory hours.
CS Elective Sentiment computing of internet
CS Elective Intelligent speech processing
Stanford CS221 Artificial Intelligence: Principles and Techniques
This is a second course in Artificial intelligence that covers selected topics in this area such as: reasoning about action and planning, uncertain and fuzzy reasoning, knowledge representation, automated reasoning, non-monotonic reasoning and answer set programming, ontologies and description logic, local search methods, Markov decision processes, autonomous agents and multi-agent systems, machine learning, reasoning about beliefs and goals, and expert systems.