Advanced Introduction to Machine Learning
10-715
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.
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, 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 |
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