Advanced Introduction to Machine Learning


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: Hsiao-Yu Fish Tung (office hours Tuesdays 3:30pm-4:30pm in GHC 8208) and Eric Wong (office hours Friday 3:00pm-4:00pm in GHC 8208)

  • Grading Policy: Homework (40%), Midterm (20%), Project (40%).

  • Google Group: Join it here. This is the place for announcements.


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 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, K-Means 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