Courseworks

EECS 504 - Computer Vision

This page contains my course work from EEECS 504 (Fall 2022)

• Programming language: Python
• Frameworks / Library: Pytorch, SciPy
• Topics:
  • Linear clssifiers
  • Gradient descent: Batch gradient descent, Stochastic gradient descent, Gradient descent with momentum, Gradient descent with RMSProp, Adam
  • Fully-connected networks
  • Convolutional networks: Alexnet, VGG, ResNet, Encoder-decoder architecture,
  • Recurrent networks: RNN, LSTMs(Long Short Term Memory)
  • Attention and transformers: Self-attention, Multi-head attention, Transformer, ViT(Vision Transformer)
  • Object detection: R-CNN, Fast R-CNN, Faster R-CNN, YOLO, FCOS,
  • Image segmentation
  • Video classification: 3D CNN
  • Generative models: GANs, VAEs(Variational AutoEncoders), Autoregressive models, Diffusion, Vector-quantized VAEs
  • Recent architectures: NeRF(Neural Radience Field), BERT(Bidirectional Encoder Representations from Transformers), Image GPT, DETR(Detection Transformer)

SI 618 - Data Manipulation and Analysis

This page contains my course work from SI 618 (Winter 2022)

• Programming language: Python
• Frameworks / Library: Pandas, Numpy, Matplotlib, Seaborn, Plotly, SciPy, Scikit-learn, Statsmodels, NLTK, spaCy
• Topics:
  • Dimension reduction: PCA(Principal Component Analysis)
  • Clustering: K-means, T-SNE, Agglomerative clustering
  • Classification: K-NN, Linear SVM(Support Vector Machine), RBF(Radial Basis Function kernel) SVM, Gaussian process classifer, Decision tree, Random forest, Neural network, DadaBoost classifier, Gaussian naive bayes classifier
  • Others: EDA(Exploratory Data Analysis), Data manipulation, Data visualization, Linear regression, NLP(natural Language Process)

STATS 500 - Statistical Learning I: Regression

This page contains my course work from STATS 500 (Fall 2022)

• Programming language: R

STATS 503 - Statistical Learning II: Modern Multivariate Analysis (In progress)

This page contains my course work from STATS 503 (Winter 2023)

• Programming language: Python
• Frameworks / Library: Numpy, Pandas, Matplotlib, Seaborn, Plotly, Missingno, Scikit-learn, XGBoost, LightGBM, Optuna, Imbalanced-Learn
• Topics:
  • Regression: Regression spline, Smoothing spline
  • Classification: KNN, LDA, QDA, Naive Bayes, Logistic regression, Kernel logistic regression, Tree based methods(Single tree, Bagging, Random forests, Boosting), SVM(Support Vector Machine), Kernel SVM(Kernel Support Vector Machine), MLP(Multilayer Perceptron)
  • Dimensionality reduction: PCA(Principal Component Analysis), Kernel PCA(Kernel Principal Component Analysis), MDS(Multi-dimensional Scaling), Isomap, LLE(Locally Linear Embedding)
  • Clustering: K-means, Hierarchical clustering, GMM(Gaussian Mixture Models), DBSCAN, Spectral clustering

STATS 504 - Practice and Communication in Applied Statistics

This page contains my course work from STATS 504 (Winter 2023)

• Programming language: Python, R
• Frameworks / Library: Numpy, Pandas, Seaborn, Matplotlib, Statmodels, SciPy, Lifelines
• Topics: Linear regression, Dimension reduction regression, SIR(Sliced Inverse Regression), Multilevel regression, GLM(Generalized Linear Model), GEE(Generalized Estimating Equations), Survival analysis, MCA(Multiple correspondence analysis), PCA(Principal Components Analysis), Power analysis, Data depth, Tail distribution, Pareto tail plot

STATS 509 - Statistical Models and Methods for Financial Data (In progress)

This page contains my course work from STATS 509 (Winter 2023)

• Programming language: R
• Topics:
  • EDA(Exploratory Data Analysis): Boxplots, Histogram, KDE(Kernel Density Estimation), QQ plot, TKDE(Transformation Kernel Density Estimation)
  • Modeling univariate distribution: Location, scale, and shape families, Skewness and kurtosis, MLE(Maximum Likelihood Estimation), Goodness-of-fit tests, Tail inference
  • Multivariate modeling: Multivariate Normal and t-distribution, Regression, Copula
  • Time series: ARIMA(Autoregressive Integrated Moving Average) model, Model selection and forecasting
  • Portfolio theory: Markowitz variance-optimal portfolios, CAPM, Fama-French factor model
  • Non-linear time series: ARCH, GARCH, ARMA-GARCH
  • Risk quantification: Measure of risk, Estimation of VaR and Expected Shortfall, Resampling and backtesting

EECS 484 - Database Management Systems

This page contains my course work from EECS 484 (Fall 2022)

• Programming language: SQL

EECS 403 - Data Structures for Scientists and Engineers

This page contains my course work from EECS 403 (Winter 2021)

• Programming language: C++

EECS 402 - Programming for Scientists and Engineers

This page contains my course work from EECS 402 (Fall 2021)

• Programming language: C++