Advanced Introduction to Machine Learning
10715
Practical information
Lectures: Monday and Wednesday, 10:30AM to 11:50AM, Location: GHC 4102
Recitations: Tuesdays 5:00PM to 6:00PM, Location: Wean Hall 8427
Instructors: Barnabas Poczos (office hours after class) and Alex Smola (office hours after class)
TAs: HsiaoYu Fish Tung (office hours Tuesdays 3:30pm4:30pm in GHC 8208)
and Eric Wong (office hours Friday 3:00pm4:00pm in GHC 8208)
Grading Policy: Homework (40%), Midterm (20%), Project (40%).
Google Group: Join it here. This is the place for announcements.
Resources
For specific contents of the class go to the individual lectures. This
is also where you'll find pointers to further reading material
etc. Please understand that uploading and processing video takes
time, both for the lecturers and also for Google, in particular when
it comes to 4k video.
Lecture slides in PDF: 1a,
1b, 2,3,4 5,6 7 8,9,10 11,12 13 14,15,16,17 18 19 20
Recitation slides in PDF recitations/rec1.pdf
YouTube playlist playlist
Homework assignments hw1 hw_1handout hw2 hw2_handout
hw3 hw3_handout hw4 hw4_handout
Solutions hw1_sol hw1_sol_code hw2_sol hw2_sol_code hw3_sol hw3_sol_code
Schedule
  Lecture  Block  Topic  Lecturer 
1  W  Sep 9  Supervised Learning  Introduction to Machine Learning, MLE, MAP, Naive Bayes  Barnabas 
2  M  Sep 14   Perceptron, Features, Stochastic Gradient Descent  Alex 
3  W  Sep 16   Neural Networks: Backprop, Layers  Alex 
4  M  Sep 21   Neural Networks: State, Memory, Representations  Alex 
5  W  Sep 23  Unsupervised Learning  Clustering, KMeans  Barnabas 
6  M  Sep 28   Expectation Maximization, Mixture of Gaussians  Barnabas 
7  W  Sep 30   Principal Component Analysis  Barnabas 
8  M  Oct 5  Kernel Machines  Convex Optimization, Duality, Linear and Quadratic Programs  Alex 
9  W  Oct 7   Support Vector Classification, Regression, Novelty Detection  Alex 
10  M  Oct 12   Features, Kernels, Hilbert Spaces  Alex 
11  W  Oct 14   Gaussian Processes 1  Barnabas 
12  M  Oct 19   Gaussian Processes 2  Barnabas 
13  W  Oct 21  Latent Space Models  Independent Component Analysis  Barnabas 
14  M  Oct 26  Graphical Models  Hidden Markov Models  Alex 
15  W  Oct 28   Directed Models  Alex 
16  M  Nov 2   Undirected Models  Alex 
17  W  Nov 4   Sampling, Markov Chain Monte Carlo Methods  Alex 
18  M  Nov 9  Midterm exam   
19  W  Nov 11  Computational Learning theory  Risk Minimization  Barnabas 
20  M  Nov 16   VC Dimension  Barnabas 
21  W  Nov 18  Nonlinear dim reduction  Manifold Learning  Barnabas 
22  M  Nov 23  Big data and Scalability  Systems for Machine Learning, Parameter server  Alex 
 W  Nov 25  Thanksgiving Holiday   
23  M  Nov 30  Project Presentations   students 
24  M  Dec 2  Project Presentations   students 

