EEM146

Machine Learning - Foundations for modeling data sources, principles of operation of common tools for data analysis, and application of tools and models to data gathering and analysis. Topics include statistical foundations, regression, classification, kernel methods, clustering, expectation maximization, principal component analysis, decision theory, reinforcement learning and deep learning.

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Complete C# Unity Developer - Game development & design. Learn C# using Unity 5 (Unity 2017 compatible). Making 2D & 3D games for web & mobile.

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Python for Data Science and Machine Learning - Usage of NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more...

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Machine Learning by Andrew Ng - Familiarized with supervised and unsupervised learning algorithms and best practices in ML – bias/variance theory, learning curves, handling skewed data, and trading off precision and recall etc. Implemented hand-written digit recognition on Octave, using concepts of neural network, one-vs-all logistic regression, and backpropagation algorithms.

EE115A

Analog Electronic Circuit - Operation of diodes and bipolar and MOS transistors. Equivalent circuits and models of semiconductor devices. Analysis and design of single-stage amplifiers. DC biasing circuits. Small-signal analysis. Operational amplifier systems.

EE141

Feedback Control Mathematical modeling of physical control systems in form of differential equations and transfer functions. Design problems, system performance indices of feedback control systems via classical techniques, root-locus and frequency-domain methods. Computer-aided solution of design problems from real world.

EE101A

Engineering Electromagnetics - Electromagnetic field concepts, waves and phasors, transmission lines and Smith chart, transient responses, vector analysis, introduction to Maxwell equations, static and quasi-static electric and magnetic fields.

EE102

Systems and Signals - Elements of differential equations, first- and second-order equations, variation of parameters method and method of undetermined coefficients, existence and uniqueness. Systems: input/output description, linearity, time-invariance, and causality. Impulse response functions, superposition and convolution integrals. Laplace transforms and system functions. Fourier series and transforms. Frequency responses, responses of systems to periodic signals. Sampling theorem.

EE113

Digital Signal Processing - Relationship between continuous-time and discrete-time signals. Z-transform. Discrete Fourier transform. Fast Fourier transform. Structures for digital filtering. Introduction to digital filter design techniques.

EE121B

Principles of Semiconductor Device Design - Introduction to principles of operation of bipolar and MOS transistors, equivalent circuits, high-frequency behavior, voltage limitations.

CS33

Computer Organization - Introductory course on computer architecture, assembly language, and operating systems fundamentals. Number systems, machine language, and assembly language. Procedure calls, stacks, interrupts, and traps. Assemblers, linkers, and loaders. Operating systems concepts: processes and process management, input/output (I/O) programming, memory management, file systems.

EE10H/110H

with labs

Circuit Theory I/II Sinusoidal excitation and phasors, AC steady state analysis, AC steady state power, network functions, poles and zeros, frequency response, mutual inductance, ideal transformer, application of Laplace transforms to circuit analysis.