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06-2024_datascraping_potaggle

algo_learning_notes

algo_learning_notes_c

autotuning

chkao831.github.io

Backend Materials for my personal GitHub page at https://chkao831.github.io/, forked from academicpages.github.io

fa17-wi18-fa20_ta-evaluation-results_ucsd-stanford

This repo contains TA evaluation reports from past students/professors.

fa17_programming-in-java-i_ucsdcse8a

Basics of programming including variables, conditionals, loops, functions/methods. Structured data storage such as arrays/lists and dictionaries, including data mutation. Hands-on experience with designing, writing, hand-tracing, compiling or interpreting, executing, testing, and debugging programs.

fa18_introduction-to-numerical-analysis_ucsdmath170a

Solving linear systems Ax = b (triangular systems, banded systems, LU and Cholesky decompositions, Gaussian elimination with and without pivoting, QR decomposition, iterative methods); perturbation theory (condition numbers and related inequalities); least squares (Gram-Schmidt, orthogonal matrices, QR decomposition); singular values (SVD decomposition); iterative methods for eigenvalues (power method, QR iteration) as well as Jacobi and Gauss-Seidel.

fa18_nlp-classifying-toxicity-in-wikipedia-comments_ucsdlign167

Implementatoin of a multi-headed NLP model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate better than Perspective’s current models. This task uses a dataset of comments from Wikipedia’s talk page edits.

fa18_nlp-deep-learning-for-natural-language-understanding_ucsdlign167

An introduction to neural network methods for analyzing linguistic data. Basic neural network architectures and optimization through backpropagation and stochastic gradient descent. Word vectors and recurrent neural networks, and their uses and limitations in modeling the structure of natural language.

fa19_numerical-linear-algebra_stanfordcme302

Solving linear systems, accuracy, stability, LU, Cholesky, QR, least squares problems, singular value decomposition, eigenvalue computation, iterative methods, Krylov subspace, Lanczos and Arnoldi processes, conjugate gradient, GMRES, direct methods for sparse matrices.

fa19_software-development-for-scientists-and-engineers_stanfordcme211

Basic usage of the Python and C/C++ programming languages are introduced and used to solve representative computational problems from various science and engineering disciplines. Software design principles including time and space complexity analysis, data structures, object-oriented design, decomposition, encapsulation, and modularity are emphasized.

fa19_statistical-methods-in-finance_stanfordstats240

Regression analysis and applications to investment models. Principal components and multivariate analysis. Likelihood inference and Bayesian methods. Financial time series. Estimation and modeling of volatilities. Statistical methods for portfolio management.

fa20_deep-learning_stanfordcs230

Neural Networks and Deep Learning; Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization; Structuring Machine Learning Projects; Convolutional Neural Networks; Sequence Models.

fa20_dl-paint-like-vincent-vangogh_stanfordcs230

Our team uses Deep Learning approaches to map from dataset Vincent Van Gogh to dataset real photo in the respect of artistic style and content. Given any content input image, our first algorithm outputs an image with a general Vincent-style filter, while the second one needs another style input image in specific to generate the corresponding output.

fa20_introduction-to-time-series-analysis_stanfordstats207

Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and state-space models. Seasonality, transformations, and introduction to financial time series.

it_learning_notes

lseg_coding_challenge

sp18_data-structure-in-java_ucsdcse12

Implementation of basic data structures including linked lists, stacks, and queues. Use of advanced structures such as binary trees and hash tables. Object-oriented design including interfaces, polymorphism, encapsulation, abstract data types, pre-/post-conditions. Recursion. Uses Java and Java Collections.

sp20_distributed-algorithms-and-optimization_cme323sparkrepo

Apache Spark - A unified analytics engine for large-scale data processing

sp20_forex-trading-final-project_stanfordmse448

Group project of MS&E 448 (Big Financial Data and Algorithmic Trading) at Stanford. It is a project course emphasizing the connection between data, models, and reality. Vast amounts of high volume, high frequency observations of financial quotes, orders and transactions are now available, and poses a unique set of challenges. This type of data will be used as the empirical basis for modeling and testing various ideas within the umbrella of algorithmic trading and quantitative modeling related to the dynamics and micro-structure of financial markets.

wi18_game-design-2048-in-java_ucsdcse8b

An implementation of graphical user interface (GUI) to create a 2048 program that mimic real-world software (e.g., simulations, games, etc.).

wi18_programming-in-java-ii_ucsdcse8b

Introductory programming using an object-oriented approach with the Java programming language. Builds on basic programming constructs introduced in CSE 8A to introduce class design and use, interfaces, basic class hierarchies, recursion, event-based programming, error reporting with exceptions, and file I/O. Basics of command-line navigation for file management and running programs. Development, testing, and debugging of more complex programs.

wi20_advanced-programming-for-scientists-and-engineers_stanfordcme212

Advanced topics in software development, debugging, and performance optimization are covered. Computer representation of integer and floating point numbers, and interoperability between C/C++ and Fortran is described. More advanced software engineering topics including: representing data in files, signals, unit and regression testing, and build automation. The use of debugging tools including static analysis, gdb, and Valgrind are introduced. An introduction to computer architecture covering processors, memory hierarchy, storage, and networking provides a foundation for understanding software performance.

wi20_mathematical-finance_stanfordmath238

Signal Generation, Factor Models and Pairs-Trading, Empirical Correlation Matrices, The Marcenko-Pastur Distribution and more.

wi21_financial-statistics_stanfordmsne349

This Ph.D. course covers topics in financial statistics with a focus on current research. Topics will include time-series modeling, volatility modeling, high-frequency statistics, large dimensional factor modeling and estimation of continuous time processes.

wi21_natural-language-processing-with-deep-learning_stanfordcs224n

Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation.

wi21_reinforcement-learning-for-stochastic-control-problems-in-finance_stanfordcme241

This course explores a few problems in Mathematical Finance through the lens of Stochastic Control, such as Portfolio Management, Derivatives Pricing/Hedging and Order Execution. For each of these problems, we formulate a suitable Markov Decision Process (MDP), develop Dynamic Programming (DP) solutions, and explore Reinforcement Learning (RL) algorithms. The course emphasizes the theory of DP/RL as well as modeling the practical nuances of these finance problems, and strengthening the understanding through plenty of coding exercises of the methods.