Lecture notes

• Lecture 1: Introduction and SVM part 1
  
• Lecture 2: SVM part 2
  
• Lecture 3: Neural Fuzzy / Hybrid intelligence
  
• Lecture 4: Hidden Markov model
  
• Lecture 5: Feature selection
  
• Lecture 6: Evolutionary Computing
  

Assignments

1. Write a linear support vector machine (10%)
   
2. Use of Hidden Markov model (10%)
   
3. Write feature selection methods and try them (10%) (no objectives yet)
   
4. Use of Evolutionary computing methods (10%) (no objectives yet)
   

Topics

• Symbolic Learning (generic term)
  
• Statistical Learning (generic term)
  
• Artificial Neural Networks (partly touched lecture 3)
  
• Support Vector Machines (lecture 1)
  
• Cluster Analysis (not covered)
  
• Fuzzy Logic (partly touched lecture 3)
  
• Evolutionary Computation (lecture 6)
  
• Hybrid Intelligent Methods (lecture 3)
  

Talked about but not covered

• Gaussian Mixture Models
  
• Random forests
  
• Bayesian Neural Networks
  
• Convex optimizations
  

Books

• Machine Learning and Data Mining: Introduction to Principles and Algorithms (same as ML1)
  
• Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, and David G. Stork
  

Evaluation

• 3 hour written exam (60%)
  
• 4 individual homework deliveries (4*10%)