Machine Learning EMI

上課連結: 週一班 週六班

PART I - Foundations Foundations

Week 1 (slides): 

       Course Logits

  What is Machine Learning?  

  Formal Model

  Lab 0: Data Flow

Week 2:    

       Formal Model

       Perceptron (I II III) 

      (自主學習) Implementation: Perceptron

Week 3:      

      Adaline and Gradient Descent

       (自主學習) Implementation : Adaline

       (自主學習) Implementation : Adaline SGD

      Realizability Assumption 

      Sample Complexity

Week 4:

      Probably Approximately Correct (PAC) Learning

      Agonostic PAC Learning 

Week 5:       

      Uniform Convergence ( 1 2 )

      General Linear Model  

      Logistic Regression 

      (自主學習)  Implementation: A General Flow of Learning Process 

Week 6: 

      No Free Lunch Theorem

      Bias-Variance TradeOff 

Week 7: 

      Spring Holiday (No Lecture)!

Week 8:   

      Midterm I  (9:30~12:00 Close Book. Held in the classroom.)

PART II - Optimization and Sparse Modeling

Week 9: 

     Convex Learning Problems

     Surrogate Function  

Week 10:     

      Stability of Strongly Convex Learning Problems 

      Support Vector Machine

Week 11: 

      Kernel Trick

      The Representer Theorem

      KNN

     Decision Tree

     (自主學習) Implementation: Kernel SVM, KNN, Decision Tree, and Random Forest 

 Week 12:  

     Data Preprocessing

     L1 Regularization 

     (自主學習) Implementation: Feature Selection

      Principle Component Analysis

PART III Deep Learning through Optimization

Week 13: 

      LDA

     Kernel PCA

     (自主學習) Implementation: Kernel PCA

     Machine Learning Pipeline and Cross-Validation 

     Learning Curve

     Imbalanced Data 

Week 14: 

     Midterm II 

Week 15: 

     Majority Voting

     (自主學習) Implementation: Majority Voting and Parameter Tuning 

     Bagging      

AdaBoost

     Clustering and K-means

     Determine the number of clusters

     Agglomerative Clustering

     DBSCAN        

Week 16: 

     Final Report

    上課時繳交: Final Exam