AL-405 Machine Learning Notes
5 notes for Machine Learning, unit by unit.
Notes for AL-405
- Unit 1 · Short notes Introduction to Machine Learning Introduction to machine learning, scope and limitations · Machine learning models · Supervised learning · Unsupervised learning · Hypothesis space and inductive bias · Evaluation methods and cross‑validation
- Unit 2 · Short notes Neural Networks Biological inspiration and simulation of neural networks · Neural network representation · Neural networks as a paradigm for parallel processing · Perceptron learning rule · Training a perceptron · Multilayer perceptron architecture
- Unit 3 · Short notes Supervised Learning Techniques Decision tree algorithms · Naïve Bayes classifier · General classification concepts · Support vector machines for classification · Random forest for classification and regression · Linear regression
- Unit 4 · Short notes Unsupervised Learning and Clustering Clustering fundamentals · k‑means algorithm · Adaptive hierarchical clustering · Gaussian mixture models · Optimization using evolutionary techniques · Determining the number of clusters
- Unit 5 · Short notes Design and Analysis of Machine Learning Experiments Design of machine learning experiments · Factors, response, and strategy of experimentation · Guidelines for machine learning experiments · Cross‑validation and resampling methods · Measuring classifier performance · Hypothesis testing for model comparison
More for AL-405
Other 4th semester AIML subjects
- AL-401 Introduction to Discrete Structure &Linear Algebra
- AL-402 Analysis & Design of Algorithms
- AL-403 Software Engineering
- AL-404 Computer Organization & Architecture
- AL-406 Java Lab