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 
9/15  course introduction  course slides  
9/17  topic 1: when can machines learn? the learning problem  course slides; LFD 1.0, 1.1.1, 1.2.4  
9/22  learning to answer yes/no  course slides; LFD 1.1.2, 3.1  
9/24  types of learning  course slides; LFD 1.2  
9/29  feasibility of learning  course slides; LFD 1.3  
10/1  topic 2: why can machines learn? training versus testing  homework 1 announced  course slides; LFD 2.0, 2.1.1 
10/6  theory of generalization  course slides; LFD 2.1.2  
10/8  the VC dimension  course slides; LFD 2.2  
10/13  noise and error  course slides; LFD 1.4  
10/15  topic 3: how can machines learn? linear regression  homework 1 due; homework 2 announced  course slides; LFD 3.2 
10/20  logistic regression  course slides; LFD 3.3  
10/22  linear models for classification  course slides; LFD 3.3 (for SGD part only)  
10/27  nonlinear transformation  course slides; LFD 3.4  
10/29  topic 4: how can machines learn better? hazard of overfitting  homework 2 due; homework 3 announced  course slides; LFD 4.0, 4.1 
11/3  regularization  course slides; LFD 4.2  
11/5  validation  course slides; LFD 4.3  
11/10  three learning principles  course slides; LFD 5  
11/12  no class and good luck with your other midterms  homework 4 announced; final project announced  
11/17  topic 5: how can machines learn by embedding numerous features? linear support vector machine  homework 3 due  course slides; LFD e8.1 
11/19  dual support vector machine  course slides; LFD e8.2  
11/24  kernel support vector machine  course slides; LFD e8.3  
11/26  no class because of ACML  
12/1  softmargin support vector machine  course slides; LFD e8.4  
12/3  kernel logistic regression  homework 4 due; homework 5 announced  course slides; extended reading: 
12/8  support vector regression  course slides; extended reading:  
12/10  topic 6: how can machines learn by combining predictive features? blending and bagging 
course slides; extended reading:  
12/15  adaptive boosting  course slides; extended reading:  
12/17  decision tree  homework 5 due; homework 6 announced  course slides; extended reading:

12/22  random forest  course slides; extended reading:  
12/24  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)  
12/29  deep learning  course slides; LFD e7.6  
12/31  deep learning (cont'd)  
1/5  radial basis function network  course slides  
1/7  gradient boosted decision tree  homework 7 due  course slides; extended reading: 
1/12  summary  course slides  
1/14  no class and good luck with your other finals  
1/19  no class because winter vacation has started (really? :) )  homework 7 due  
1/21  final project report due 
Last updated at CST 13:08, October 04, 2023 Please feel free to contact me: 