This class is an introductory graduate course in machine learning. The class will briefly cover topics in regression, classification, mixture models, neural networks, deep learning, ensemble methods and structure prediction.
Prerequisites: A good knowledge of statistics, linear algebra and calculus is highly recommended as well as good programming skills.
Each student will need to do the homework, exam and complete a project (done individually or in pairs).
|The final grade will consist of the following|
Each student will need to write a short project proposal (due March 5th). The projects will be research oriented. The proposal has to be approved by the instructor. By April 10th the final project report (in the form of a paper) will need to be handed in. Additionally, there will be a short, roughly 10 min, presentation.
The students can work on projects individually or in pairs. The project can be an interesting topic that the student comes up with himself/herself or with the help of the instructor (list of projects available here). The grade will depend on the ideas, how well you present them in the report, how well you position your work in the related literature, how thorough are your experiments and how thoughful are your conclusions.
Click on the syllabus
|Jan 5||Course Introduction||Richard Zemel||lecture1|
|Jan 12||Regression||Bishop, Chapter 1.0-1.1; 3.1||Raquel Urtasun||lecture2|
|Jan 13||Review on Probability Tutorial||TA: Shenlong Wang||tutorial1|
|Jan 19||Discriminative Classification||Bishop: 120-127 , 179-195||Raquel Urtasun||lecture3|
|Jan 20||Optimization Tutorial||TA: Shenlong Wang||tutorial2|
|Jan 26||Generative Classification||Bishop 4.1.2, 4.3.4, 4.2.2, 380-381||Raquel Urtasun||lecture4|
|Jan 27||Linear Regression and NN Tutorial||TA: Ali Punjani||tutorial3|
|Feb 2||Neural Networks||Richard Zemel||lecture5|
|Feb 3||Neural Network Examples Tutorial||TA: Ali Punjani||tutorial4|
|Feb 9||Clustering, MOG, EM||Richard Zemel||lecture6|
|Feb 10||MOG Tutorial||TA: Shenlong Wang|
|Feb 16||NO CLASS|
|Feb 17||NO TUTORIAL|
|Feb 23||Continuous Latent Variable Models||Alexander Schwing||lecture7|
|Feb 24||PCA Tutorial||TA: Ali Punjani||tutorial5|
|March 2||Ensemble Methods||Richard Zemel||lecture8|
|March 9||SVMs and kernels||Raquel Urtasun||lecture9|
|March 16||Topics: Structured Prediction||Raquel Urtasun||lecture10|
|March 23||Topics: Deep Learning||Raquel Urtasun||lecture11|
Project Instructions: (out Feb. 26, due April 10 at noon) > Instructions