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Machine Learning and Data Mining Syllabus
Winter 2018
Instructor Information
Instructor Email Office Location & Hours
Dr. Ahmad Alhindi [email protected] CCIS Building,1st floor, Room 1118, 8am-12pm, Mondays
General Information
Description
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning and reinforcement learning. The course will also discuss recent applications of machine learning such as data mining, language processing, speech recognition, and text and web data processing.
This course will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques
Expectations and Goals
On completion of the course, students should be able to:
Demonstrate an understanding of the common kinds of problems solved through machine learning: classification, regression and Clustering.
demonstrate an understanding of the common machine learning techniques/algorithms that have important practical applications
identify machine learning techniques appropriate for particular classes of problem and apply them to practical problems
undertake a comparative evaluation of several machine learning procedure
Course Materials
There is no required text for this course. Supplemental notes for some topics will be posted periodically on the University e-learn system. Lectures are generally self-contained, but for additional reading, the following references are freely available online:
Daume, A Course in Machine Learning.
Ian Goodfellow, Yoshua Bengio and Aarona Courville, Deep Learning.
Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning (Links to an external site.)
Microsoft AI School,
The following textbooks are good quality, but in some cases more advanced or mathematical that this course:
Bishop, Pattern Recognition and Machine Learning (Links to an external site.)
Rogers and Girolami, A First Course in Machine Learning
Vijay Kotu and Bala Deshpande, Predictive Analytics and Data Mining.
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Course Topic
Topic Note
Background information Introduction
Notation and terminology Math and statistics refresh
Search and optimization Optimization problems continues & discreet spaces Local search Methods
Genetic-based learning Genetic algorithms Predicting categories
(classification) Learning to classify (predicting categories)
Decision tree Bayesian networks Support vector machine k-nearest neighbor Predicting values
(regression)
Learning to predict values
Linear regression Regression tree Discovering structure
(clustering)
k-means algorithm
Agglomerative hierarchical methods Association rules mining A priori algorithm
Frequent Pattern (FP)-Growth algorithm
Multiple learners Bagging
Boosting Forest
Deep learning Neural network
Convolutional Networks Recurrent Networks
Generative Adversarial Networks Concentrations
Applications/Projects/Research
Text Mining
Time Series Forecasting Anomaly Detection Feature Selections Reinforcement Learning
Note
The instructor reserve the right to make changes to this syllabus as necessary.
