Machine Learning Algorithms & Evaluation

Choosing the Right AI Approaches for Business Problems

Navigate the complex landscape of machine learning applications with confidence in this comprehensive session. Discover how machine learning addresses diverse business challenges through classification, regression, clustering, recommendation, and forecasting. We learn through examples of how these techniques power real-world solutions. 

We explore how computer vision transforms visual data into insights, how natural language processing unlocks value from text, and examine case studies showcasing AI's impact across industries.

We share the critical evaluation metrics professionals use to assess model performance beyond simple accuracy, including generalization ability, robustness against manipulation, explainability for stakeholder trust, and practical considerations like complexity and training requirements. 

The session rounds out with an overview of a range of algorithms for common use cases, explained in accessible business language. Essential knowledge for decision-makers who need to understand algorithmic options when evaluating AI solutions or communicating with technical teams.

• Outline
• Machine Learning application types
• Classification
• Regression
• Clustering
• Recommendation
• Forecasting
• Machine Learning applications
• Computer Vision
• Natural Language Processing
• Industry applications
• Model evaluation and performance metrics
• Generalization
• Robustness
• Explainability
• Complexity
• Training Time
• Machine Learning Algorithms: 
• Logistic Regression
• K-Means Clustering
• Naive Bayes
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Random Forest
• Gradient Boosting Machines (GBM) / XGBoost
• Autoregressive Integrated Moving Average (ARIMA)

• Format
• This session is lecture-based.

• Level
• Intermediate

• Prerequisite Knowledge
• Machine Learning Fundamentals

• Learning Outcomes
• Knowledge of machine learning applications, evaluation metrics and advanced algorithms