Courses I have been teaching at Meharry School of Applied Computational Sciences

Topics Covered:  

CNN, RNN, LSTM, Transfer Learning, GAN, Transformer

Topics Covered:  

Linear Regression,  Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Network, Convolutional Neural Net, Principal Component Analysis and Clustering, Hyper-parameter Tuning - Bias Variance, Model Performance Evaluation 

Topics Covered:  

Linear Regression,  Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Network, Convolutional Neural Net, Principal Component Analysis and Clustering, Hyper-parameter Tuning - Bias Variance, Model Performance Evaluation 

Topics Covered:

Analyzing Contingency Tables, Regression Models, Exploratory Data Analysis, Model Development Basics, Predictive modeling for categorical values: classification models (logistic regression, classification trees, random forest, Bayes classifier), Predictive modeling for numerical values, Neural Networks for Predictive Modeling, Hyperparameter Tuning - Gradient Descent, Bias Variance Trade-off, Train Test Split, Predictive modeling for time series data

Topics Covered: 

Literature Review Overview, Meta-Analysis Overview, Hypothesis Formulation, Data Collection and Processing Pathway, Knowledge Transfer Basics, Developing Presentation Skills, Introduction to Writing Research Proposal, Plagiarism and Citation Management, How to Write a Manuscript, How to Present a Scientific Research Poster

Evaluations from students: