1) The class will be remote-only
2) Please refer to the end of this page for some COVID-specific rules and requests.
Note: all enrollment is by instructor's consent. E-mail email@example.com to request consent (include information on your program/year and, as relevant, on your academic background pertinent to course prerequisites).
There will be an math prerequisite assessment (an "entrance exam") administered in the first week of quarter. Consent will be given after the first week, based on the assessment results, course space availability, and other considerations.
A systematic introduction to machine learning, covering theoretical as well as practical aspects of the use of statistical methods. Topics include linear models for classification and regression, support vector machines, regularization and model selection, and introduction to structured prediction and deep learning. Application examples are taken from areas like information retrieval, natural language processing, computer vision and others.
Prerequisites: Probability, Linear Algebra, Undergraduate Algorithms.
A detailed topic list (roughly in order of presentation)
- intro to ML: scope, motivation, and goals of the class
- refresher on probability and algebra (TA)
- statistical framework for learning; loss/risk; least squares regression
- noise models; error decomposition; bias/variance and overfitting
- model complexity, sparsity (L1/L2) in regression;
- classification; Fisher’s LDA, logistic regression and softmax
- ensemble methods, boosting, stepwise methods
- generative models, Naive Bayes, multivariate Gaussians
- EM for mixture models and in general
- SVM and kernels
- nonparametric methods; nearest neighbors, density estimation
- multilayer neural networks and deep learning
- information theory and learning; information criteria, MDL and their connections to regularization
- experiment design and evaluation in ML
- advanced topics (time permitting)
- wrap-up and review of the class
- Understand the notion of fitting a model to data and concepts such as model complexity, overfitting and generalization, and bias-variance tradeoff in estimation.
- Learn and be able to apply some of the fundamental learning methods, such as logistic regression, support vector machines, boosting, decision trees, neural networks.
- Learn the basics of optimization techniques such as gradient descent and the general EM algorithm.
- Familiarity with multivariate Gaussians and mixtures of Gaussians.
- Understand fundamental concepts in information theory (entropy, KL-divergence) and their relationship to machine learning.
COVID-19 related rules and requests , in case any in person components are included in the course:
If you feel sick: stay home. Anyone experiencing symptoms of COVID-19 can contact the UChicago Medicine COVID-19 triage hotline for screening at 773.702.2800. Notify firstname.lastname@example.org if you test positive for COVID-19, even if you are asymptomatic.
When physically attending class or office hours:
Wear an appropriate mask or face covering (completely covering nose and mouth) any time you are in a public area or an area where you may see other people. Your instructor at TTIC reserves the right to determine what an appropriate facial covering is.
Follow general social distancing and sanitation guidelines per UChicago and TTIC protocols.
When arriving on campus, proceed directly to your classroom and sit down. Seats will be pre-arranged to allow for social distancing. Do not rearrange seats. When class is over, leave the classroom as soon as possible, maintaining social distancing.
Talk to your instructor if you have concerns specific to your circumstances, such as a health condition that places you or someone in your household at high risk. If you need accommodations due to an existing health condition, contact Amy Minick, TTIC’s Director of Human Resources, at email@example.com or 773.702.5033.
Be kind. Understand that this is a stressful time for everyone, and an extra bit of kindness right now can go a long way.
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