The main objective of this course is to teach fundamental techniques in machine learning. Key components include statistical learning approaches, including but not limited to various parametric and nonparametric methods for supervised and unsupervised learning problems. Particular focuses on the theoretical understanding of these methods, as well as their computational implications.
Undergraduate level training or coursework in linear algebra, calculus and multivariate calculus, basic probability and statistics; an undergraduate level course in artificial intelligence may be helpful but is not required.
As you may have already known, there will be an entrance exam at the first day of the class (Jan 11, 2016). The exam is to survey the students knowledge on basic concepts required for machine learning techniques so that we can better prepare the teaching materials.
Due to the sitting limitation and the large number of students who are interested in this course, we may not have enough seats for those who are on the waiting list. Therefore we will host the exam on a first-come-first-serve basis. To help you to prepare the exam, you can review the following topics - linear algebra, calculus, basic probability and statistics.
The exam is closed-book. We will distribute the exam and answer sheets. You only need to bring writing tools (pens or pencils). You will need to take the exam, whether you are registered or still on waiting list. There is no exception to the rule.
A sample quiz is available here.
There will be no required textbooks. However, we suggest one of the following to help you to study:
We will mark suggested readings from these two books.
Date | Topics | Assignments (tentative) |
---|---|---|
1/11/2016 | Entrance Exam, Overview of ML, Review of Basic Math Concepts | |
1/18/2016 | University Holiday - Martin Luther King Day | |
1/25/2016 | Density Estimation, Nearest Neighbors, Linear Regression | HW#1 out |
2/1/2016 | Decision trees, Naïve Bayes, Logistic Regression | |
2/8/2016 | Linear/Gaussian Discriminant Analysis, Overfitting and Regularization, Bias-variance tradeoff | HW#1 due, HW#2 out |
2/15/2016 | University Holiday - Presidents’ Day | |
2/22/2016 | Kernel Methods, SVM | HW#2 due, HW#3 out |
2/29/2016 | Geometric Understanding of SVM, Boosting | |
3/7/2016 | Quiz 1 , Pragmatics: Comparing and evaluating classifiers | |
3/14/2016 | Spring break - Holiday | HW#3 due |
3/21/2016 | Neural Networks and Deep Learning | Mini Project details out, HW#4 out |
3/28/2016 | Clustering, Mixture models, EM algorithm | |
4/4/2016 | Large-scale Learning for Big Data, Dimensionality Reduction | HW#4 due, HW#5 out |
4/11/2016 | Kernal PCA, HMM | Mid-term report due |
4/18/2016 | Graphical Models, Recommender Systems, Course Review | HW#5 due |
4/25/2016 | Quiz 2 (Last week of classes) | |
5/2/2016 | Mini Project | Mini Project - Final Report due |