Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the data observed. This course introduces the basics of learning theories, the design and analysis of learning algorithms, and some applications of machine learning.
date  syllabus  todo/done  suggested reading 
09/15  course introduction  course slides  
09/18 
topic 1: when can machines learn? the learning problem; 
course slides; LFD 1.0, 1.1.1, 1.2.4  
09/22  learning to answer yes/no 
course slides; LFD 1.1.2, 3.1 ; course slides on machine learning and artificial intelligence 

09/25  types of learning  homework 1 announced 
notes about convergence of PLA ; course slides; LFD 1.2; LFD 1.3 
09/26  no class on this makeup day because it will be ineffective to take four hours within two days  
09/29  feasibility of learning 
notes about proof of Hoeffding ; course slides; LFD 1.3 

10/02  no class because of longweekend of midautumn festival  
10/06 
topic 2: why can machines learn? training versus testing 
course slides; LFD 2.0, 2.1.1  
10/09  no class because of longweekend of doubleten holiday 


10/13  the VC dimension  course slides; LFD 2.2  
10/16  noise and error  homework 1 due; homework 2 announced  course slides; LFD 1.4 
10/20 
topic 3: how can machines learn? linear regression 
course slides; LFD 3.2  
10/23  logistic regression  course slides; LFD 3.3  
10/27  linear models for classification  course slides; LFD 3.3 (for SGD part only)  
10/30  nonlinear transformation  homework 2 due; homework 3 announced  course slides; LFD 3.4 
11/03 
topic 4: how can machines learn better? hazard of overfitting 
course slides; LFD 4.0, 4.1  
11/06  regularization  course slides; LFD 4.2  
11/10  validation  course slides; LFD 4.3  
11/13  three learning principles; machine learning for modern artificial intelligence 
homework 3 due; homework 4 announced 
course slides; LFD 5; course slides 
date  syllabus  todo/done  suggested reading 
11/17 
topic 4: how can machines learn by embedding numerous features? linear support vector machine 
course slides; LFD e8.1  
11/20  no class because of NTU sports day  
11/24 
dual support vector machine; kernel support vector machine 
course slides; LFD e8.2; course slides; LFD e8.3 

11/27  softmargin support vector machine  homework 4 due; homework 5 announced  course slides; LFD e8.4 
12/01  topic 7: how can machines learn by distilling hidden features? neural network 
course slides; LFD e7.1, e7.2, e7.3, e7.4 (selected parts)  
12/04  matrix factorization 
course slides; extended reading: 

12/08  guest lecture: Machine Learning and Data in Big Tech Companies by Dr. Scott Chen  
12/11  no class because of NeurIPS 2020  
12/15  neural networks, matrix factorization (unfinished parts)  
12/18 
topic 2: how can machines learn by combining predictive features? blending and bagging 
course slides; extended reading: 

12/22  adaptive boosting 
course slides; extended reading: 

12/25  decision tree (selected) and random forest (selected)  homework 5 due; homework 6 announced  course slides; course slides; extended reading: 
12/29  gradient boosted decision tree; deep learning basics (selected)  course slides; course slides; LFD e7.6 extended reading: 

01/01  no class because of longweekend of new year's day  
01/05  modern deep learning: activation 
course slides; extended reading:


01/08  modern deep learning: initialization, optimization, regularization 
course slides; extended reading:
extended reading:
extended reading: 

01/12  finale and award ceremony  course slides  
01/15  no class and winter vacation started (really?)  homework 6 due  
01/19  no class and winter vacation started (really?)  final project due 