Lectures related to Gene prediction

Linear Regression: Estimation and Prediction

Covers the basics of linear regression, focusing on estimation and prediction.

Predicting completion time: Activity Progress

Covers the prediction of completion time in activities based on progress data.

Metabolic Engineering: Systems Biology and Tools

Explores systems biology of metabolism, genome-scale models, and metabolic engineering applications.

Hidden Markov Models (HMM): Theory

Covers Hidden Markov Models (HMM) for modeling time series data and decoding using the Viterbi Algorithm.

Advanced Physics I: Introduction to Mechanics

Introduces the basics of physics, including mechanics and making predictions based on observations and hypotheses.

Advanced Physics I: Brayton Cycle and Air Density

Explores the Brayton cycle, air density variations, and forces in motion.

Protein Design: Enzyme and Biosensor Engineering

Explores novel enzyme and biosensor design for catalysis and ligand detection.

Kalman Filter: Time Series

Covers structural modeling, state space models, and the Kalman filter in time series analysis.

Machine-learned force fields for molecular simulation

Explores machine-learned force fields for accurate molecular simulations and the solution of the electronic Schrödinger equation.

Chemical Reaction Prediction: Molecular Transformer

Explores chemical reaction prediction using generative models and molecular transformers, emphasizing the importance of molecular language processing and stereochemistry.

Data Driven Science: MODNet Methodology

Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.

Principal Components Analysis

Covers Principal Components Analysis, a technique for dimensionality reduction and gene type analysis.

Kernel Methods: Recap and Applications

Covers polynomial feature expansion, kernel methods, data representations, normalization, and handling imbalanced data in machine learning.

Improving Density Predictions in Machine Learning

Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.

Genomic Data Analysis: Clustering and Survival

Explores genomic data clustering, survival analysis, gene identification, and statistical significance in cancer research.

Single cell RNA-sequencing: Methods and Data Analysis

Covers the process of single cell RNA-sequencing and clustering data.

Property Testing: Uniform Distribution

Covers property testing for uniform distributions and the importance of reliable predictions.

Linear Regression and Gradient Descent

Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.

Set Cover: Integrality Gap

Explores the integrality gap concept in set cover and multiplicative weights algorithms.

Jordan Form: Theory and Examples

Covers the theory and examples of Jordan forms and their practical application in matrix analysis.