Lectures related to Machine Learning-Guided Treatment Discovery

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Gaussian Naive Bayes & K-NN

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Predicting New Product Life Cycles: Machine Learning Approach

Explores machine learning for predicting new product life cycles and the challenges of limited historical data.

Predicting Bitcoin's Price with ML and Twitter Inputs

Showcases a project predicting Bitcoin's price using Twitter and ML, achieving 60% accuracy.

Feedback & Adaptation

Explores feedback and adaptation in visual intelligence, enhancing machine performance in dynamic environments.

Regression: High Dimensions

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Engineering in the Age of AI: Innovations and Challenges

Examines the transformative impact of AI on engineering disciplines and the associated challenges.

Overfitting: Symptoms and Characteristics

Explores overfitting in polynomial regression, emphasizing the importance of generalization in machine learning and statistics.

World of Data: Machine Learning and Value Chain

Delves into machine learning, data insights, and commercial value in the digital world.

Data Mining: Introduction

Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.

Decision Trees and Random Forests: Concepts and Applications

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Smart, Connected Products

Explores smart, connected products and their transformative impact on companies, covering artificial intelligence, machine learning, predictive models, forecasting methods, and more.

Introduction to Machine Learning

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Linear Regression: Foundations and Applications

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Generative Models: Advancements in Molecular Design

Explores the use of generative models for discovering novel molecules in molecular design.

Machine Learning-Guided Treatment Discovery

Explores personalized medicine, machine learning for treatment prediction, and challenges in traditional algorithms.

Automated Chemical Synthesis: Catalyst Design & Optimization

Delves into the automation of chemical synthesis through catalyst discovery and optimization using machine learning and computational chemistry.