| I. Course description | II. Course textbooks | III. Course outline |
| IV. Grading information |
The information explosion of the past few years has us drowning in data but often starved of knowledge. Many companies that gather huge amounts of electronic data have now begun applying data mining techniques to their data warehouses to discover and extract pieces of information useful for making smart business decisions.
In this course we will study a variety of techniques for data mining and machine learning. Some emphasis will be given to approaches that are scalable to very large data sets and/or those that are relatively robust when faced with a large number of predictors, and algorithms for heterogeneous or streaming data. Many of these capabilities are essential for handing BIG DATA. We will mostly be using Python (specially, Scikit-Learn), though some problems in R will also be given.
The central goal of this course is to convey an understanding of the pros and cons of different data mining techniques, so that you can (i) make an informed decision on what approaches to consider when faced with real-life problems requiring predictive modeling, (ii) apply models properly on real datasets so to make valid conclusions. This goal will be reinforced through both theory and hands-on experience.The material for the lectures is taken from a wide variety of sources. My notes will be available via Canvas. The textbooks for the course are:
Relevant sections of these books are indicated next to the topics below. Topics without reference to KJ, JW will be covered through lecture notes and a reading list of papers.
Supplementary texbooks are:
KJ, JW, HTF and B refer to the texts and supplementary books
1. Overview: Data Mining process; Local vs. global models, and other trade-offs.
(2 lectures; KJ: Chapter 1,2; JW Chapters 1-3, B Ch 1, 2.1-2.3; HTF Ch 1, 2.1-2.6)
Objective: Provide overview and context for this class.
2. Regression:Multiple Linear Regression; Basis function expansion; Dealing with large number of features; Ridge, Lasso and Stagewise Approaches; Non-linear methods; Bayesian approach; and Multilevel models. User's guide.
(3 lectures;
KJ: Chapter 4-6, 7.1,7.4, 7.5; JW Ch 6, B 3.1, 3.2; HTF Ch 2.7, 2.8, 3.1-3.4, 7.1-7.3, 11.1-11.8)
Objective: Learn to understand predictive models where desired outcome is a numeric quantity.
3. Classification: Bayes decision theory, Naïve Bayes and Bayesian networks; (Scaled) Logistic regression; LDA; Scaling decision trees to big data; Kernel methods and Support Vector Machines (SVMs) for classification and regression; Deep Learning and Tensorflow; dealing with class imbalance
(6 lectures,
KJ: Chapter 11,12,13,14.1,14.2,8.1,16; JW 4, 8, 9; B 4.1-4.3.4; 6.1, 6.2, 7.1, 14.4; HTF Ch 4, 7.10, 9.2, 12, 13.3
Objective: Learn to understand predictive models where desired outcome is a class label.
4. Data Pre-Processing: Transformations, Imputations, Sampling, Outlier detection
(3 lectures, KJ: Chapter 3, JW 10.1, 10.2, B 12.1; HTF Ch 14.5, 14.8
Objective: Understand that good data quality is a pre-requisite for effective models, and study some methods for improving data quality.
5 Modern Interactive Visualization:Javascript, Bokeh, R-Studio. Visual Analytics.
(Interspersed with Topics 2 and 3 as needed)
Objective: Visualization of Data as well as model outcomes for better understanding and communication. Visual analytics.
6. Ensemble Methods: Model Averaging, Bagging and Random forests, boosting, Gradient boosting; Bag of Little Bootstraps
(2 lectures; KJ: Chapter 14.3-14.8; JW 8.2; B 14.2, 14.3, HTF Ch 8.7, 8.8, 10.1-10.7, 16
Objective: Understand the benefits of combining multiple predictive models.
7. Clustering: k-means; hierarchical methods, graph partitioning; co-clustering, semi-supervised learning. Market Basket applications
(3 lectures; JW 10.3, 10.5, B 9.1, 9.2; HTF Ch 13.1, 13.2, 14.3, 14.4)
Objective:
Learn issues involved in unsupervised learning and the trade-offs among alternative approaches to clustering.
8. Specialized/Advanced Topics: (coverage depends on time available and interest of class)
(a) Multi-task Learning: Predictive analytics for (multi)-relational data; Use of auxiliary information such as networks;
(b) Stochastic BlockModels. Applications to Next Generation Recommender Systems; Customer-product affinity detection;
(c) Ranking; Spam filtering
(d) Distributed Data Mining. Overview; ADMM and distributed logistic regression
(e) Stream Data Mining. more SGD type algorithms
(as time permits)
Objective: Study some important specialized situations where predictive models are deployed, including large-scale predictive modeling using multiple machines, e.g. cloud computing.
Term Project Presentations and Discussion
(4 lectures)
| 10+20% | Project (groups of 3-5): (project outline + 2 presentations) + term paper due May 10th, 11:59pm |
| 20% | 6 Asssignments |
| 15% | 4 pop-quizzes. Best 3 scores counted |
| 30% | Two 1-hour tests in class. (3/7; 4/13) |
| 5% | Class participation |
There will be no final exam.
Quizzes will be held in class and of duration 15-30 minutes. Their objective is to review key concepts introduced in class.
At the end of the course, you will get a score out of 100 based on the percentages stated above. Your final grade will be solely based on this score. The grade is primarily based on the curve, i.e. is relative to how the whole class performs; however entire curve may shift up or down a bit depending on how the class as a whole performs relative to past classes. Grading is NOT based on absolute thresholds, e.g. 90+ = A etc.
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