PHARM.D. & M.S. in ARTIFICAL INTELLIGENCE

We experience artificial intelligence (AI) every day in our interactions with the world: voice recognition embedded in our phones, advertisements customized to our e-mail accounts based on e-mail content and Google search history, and self-driving cars. The more a software application is used, the more it “learns” from the available data, and the more accurate the application’s responses become. Technology’s ability to self-improve is the essence of machine learning. AI already plays a role in health informatics, discovery and design of new drugs, and personalized medicine, and will one day allow clinical trials to be conducted virtually. The modern pharmacist must be conversant with the subjects included in this M.S. degree option, including computational sciences, bioinformatics, “big data” mining and analysis, robotics, statistical learning, and deep learning.

M.S. in Artificial Intelligence (30 credits)

All courses are three (3) credit hours, coded as accessible, advanced or challenging

Electives (6 credits):

Non-thesis option: Two (2) elective courses from Depts. of AI, Computer Science or Data Analytics

Thesis option: Six (6) credits of AI thesis research

Course descriptions:

AI 602: Programming in Python
Problem solving, algorithmic design, and implementation using the Python programming language are presented. Topics include fundamental data types and associated collection data types, I/O processing, conditional and loop constructs, and use and implementation of functions. This first part of the course is complemented with a through presentation of object-oriented programming. Select advanced features for both procedural programming and object-oriented programming are introduced. Throughout the course, sound programming styles and development are emphasized. Three credits; one-hour laboratory. Prerequisites: none.

AI 632: Algorithms and Data Structures in Python
A comprehensive study of the design and analysis of efficient data structures and algorithms in Python. The course provides the fundamentals of data structures and algorithms, including their design, analysis and implementation. Fundamental data abstractions include linear lists, stacks, queues and deques, priority queues, multi-linked structures, trees and graphs, maps, hash tables, and internal and external sorting and searching.  Three credits; one-hour laboratory.  Prerequisite: AI 602.

AI 680: Artificial Intelligence: Present and Future
AI systems now outperform humans on tasks that were once thought to require a uniquely human intelligence (e.g., playing chess). How far can this go in the future? What are the assumptions behind different approaches to AI? What dangers can there be from AI systems, and how should AI practitioners take these into account? The course gives a quick overview of the background and contemporary work in symbolic AI, and looks at the relationship between statistical and two logical approaches to AI. It also addresses some of the philosophical and ethical issues that arise. The course surveys the state of the art in current AI, looking at systems and techniques in various subfields (agents and reasoning; planning, constraints and uncertainty; Google search and the semantic web; dialogue and machine translation; varieties of learning). Three credits; one-hour laboratory. Prerequisites: none.

AI 681: Machine Learning and Pattern Recognition
Fundamental theoretical concepts in machine learning and common patterns for implementing methods in practice are discussed. The course is intended for those wanting the background required to begin research and development on machine learning methods. The course provides foundations of pattern recognition algorithms and machines, including statistical and structural methods. Data structures for pattern representation, feature discovery and selection, classification vs. description, parametric and non-parametric classification, supervised and unsupervised learning, use of contextual evidence, clustering, recognition with strings, and small sample-size problems are covered. Three credits; one-hour laboratory. Prerequisites: none.

AI 682: Data Mining and Exploration
The aim of this course is to discuss modern techniques for analyzing, interpreting, visualizing and exploiting the data that are captured in scientific and commercial environments. The course will develop the ideas taught in various machine learning courses and discuss the issues in their application to real-world data sets, and will cover other techniques and data visualization methods. The first part of the course is lecture-based; occasional guest lectures from data mining practitioners are anticipated. This will be followed by student presentations of papers relating to relevant topics. Students will also carry out a practical mini-project on a real-world dataset. For both paper presentations and mini-projects, lists of suggestions will be available, but students may also propose their own, subject to approval from the instructor. Three credits; one-hour laboratory. Prerequisites: none.

AI 683: Statistical Learning
This course provides an introduction to the statistical methods commonly used in learning from data. Methodology with theoretical foundations and their computational aspects are covered. The course aims to assist in designing “good” learning algorithms and analyzing their statistical properties and performance guarantees. Fundamental principles and techniques of probabilistic thinking, statistical modeling, and data analysis are introduced. Topics covered include basic probability and statistics, including events, conditional probabilities, Bayes theorem, random variables, probability distributions, and hypothesis testing. Building on these concepts, the course provides in-depth coverage of supervised learning from data with focus on regression and classification methods. A few key unsupervised learning methods such as clustering (K-means and hierarchical clustering) are covered.  The “R” computer language is used throughout the course. Three credits; one-hour laboratory. Prerequisites: none.

AI 686: Automatic Speech Recognition
The course covers the theory and practice of automatic speech recognition (ASR), with a focus on the statistical approaches that comprise the state of the art. The course introduces the overall framework for speech recognition, including speech signal analysis, acoustic modelling using hidden Markov models, language modelling and recognition search. Advanced topics covered will include speaker adaptation, robust speech recognition and speaker identification. The practical side of the course will involve the development of a speech recognition system using a speech recognition software toolkit. Three credits; one-hour laboratory. Prerequisites: none.

AI 687: AI and Machine Learning in Bioinformatics
The digital revolution has seen a dramatic increase in data collection in various disciplines of the health sciences. The challenge of “big data” and “wide data” is especially pronounced in the biomedical space where, for example, whole-genome sequencing technology enables researchers to interrogate all 3 billion base pairs of the human genome. With an expected 50% of the world’s population likely to have been sequenced by 2025, the resulting datasets may surpass those generated in astronomy, Twitter and YouTube combined. Machine learning approaches are hence necessary to gain insights from these enormous and highly complex modern datasets, enabling the training of very sophisticated machine learning models in the context of artificial intelligence.

The course addresses various machine learning approaches that have been applied during the genomic revolution. Emphasis is placed on machine learning algorithms to recognize patterns in DNA sequences such as pinpointing the locations of transcription start sites (TSSs), identifying the importance of junk DNA in the genome, and identifying untranslated regions (UTRs), introns and exons in eukaryotic chromosomes.  The input data can include the genomic sequence, gene expression profiles across various experimental conditions or phenotypes, protein-protein interaction data, synthetic lethality data, open chromatin data, and ChIP-seq data. Three credits; one-hour laboratory. Prerequisites: AI 681.

AI 688: Image and Vision Computing
The course addresses the analysis of images and video in order to recognize, reconstruct and model objects in the three-dimensional world. Emphasis is placed on studying the geometry of image formation; basic concepts in image processing such as smoothing, edge and feature detection, color, and texture; motion estimation; segmentation; stereo vision; 3-D modeling; statistical recognition. Three credits; one-hour laboratory. Prerequisites: AI 681.

AI 689: Computational Neuroscience, Cognition and Artificial Intelligence
The course addresses foundational tools that connect cognitive science and computational neuroscience with artificial intelligence. Emphasis is placed on computational models that mimic brain information processing during perceptual, cognitive and control tasks tested with brain and behavioral data. Computational approaches to understanding cognitive processes using massively parallel networks are studied. Biologically-inspired learning rules for connectionist networks and their application in connectionist models of perception, memory and language are discussed. Three credits; one-hour laboratory. Prerequisites: AI 681.

AI 698: Research Thesis I
Preparation of a thesis under the supervision of a faculty adviser. The completed thesis is evaluated by the Department's Graduate Curriculum Committee. Three credits.

AI 699: Research Thesis II
Preparation of a thesis under the supervision of a faculty adviser. The completed thesis is evaluated by the Department's Graduate Curriculum Committee. Three credits.

AI 700: Applicable Deep Learning
Deep learning is one of the most highly sought-after skills in AI. In this course, the student will learn the foundations of deep learning, how to build neural networks, and how to lead successful machine learning projects. The course covers convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.

Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance on a given task. For example, asked to recognize faces, a deep neural network may learn to represent image pixels first with edges, followed by larger shapes, then parts of the face like eyes and ears, and, finally, individual face identities. Deep learning is behind many recent advances in AI, including Siri’s speech recognition, Facebook’s tag suggestions and self-driving cars. The student will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing, and will master not only the theory but also see how it is applied in industry. These ideas will be explored using Python and TensorFlow. The student will likely find creative ways to apply what is learned to her/his career. This course culminates in a capstone project. Three credits; one-hour laboratory. Prerequisites: AI 681.

AI 701: Intelligent Autonomous Robotics
This course covers basic topics in autonomous robotics/systems. Intelligent autonomous robots and systems can sense their environment, make decisions on how to act based on the sensations, and execute these actions without human aid or intervention. The main focus of the course is on designing and building robotic systems that navigate independently in complex environments. It is a programming-intensive course that requires teamwork and collaboration, the use of the robotic hardware interface, and the implementation of several algorithms to address key areas for effective sensor processing, vision processing, and autonomous decision-making in a physical setting or a 3D-simulated environment. Three credits; one-hour laboratory. Prerequisites: AI 688 and AI 700.

AI 790: Special Topics in Artificial Intelligence I
A course for presenting timely advanced topics in artificial intelligence, including research topics. The topics may vary from year to year according to the interest of faculty and students. The course contents and objectives are aligned with the overall program learning goals. The course requires formal submission of the course topic and a detailed syllabus for department and faculty reviews and approvals. Three credits; one-hour laboratory. Prerequisites: AI 680 and AI 681.

AI 791: Special Topics in Artificial Intelligence II
A course for presenting timely advanced topics in artificial intelligence, including research topics. The topics may vary from year to year according to the interest of faculty and students. The course contents and objectives are aligned with the overall program learning goals. The course requires formal submission of the course topic and a detailed syllabus for department and faculty reviews and approvals. Three credits; one-hour laboratory. Prerequisites: AI 680 and AI 681. 

4-YEAR PLAN

Red: AI graduate courses that replace Pharm.D. core courses

Blue: AI graduate courses that replace Pharm.D. electives

Green: Additional AI courses required for the M.S. degree

NOTE: 18 credits are included within the Pharm.D. curriculum; shared credit degree students will need to take and pay for an additional 12 credits.

Red: AI graduate courses that replace Pharm.D. core courses

Blue: AI graduate courses that replace Pharm.D. electives

Green: Additional AI courses required for the M.S. degree

5 YEAR PLAN

Red: AI graduate courses that replace Pharm.D. core courses

Blue: AI graduate courses that replace Pharm.D. electives

Green: Additional AI courses required for the M.S. degree

NOTE: 9 credits are included within the Pharm.D. curriculum; shared credit degree students will need to take and pay for an additional 21 credits.

Red: AI graduate courses that replace Pharm.D. core courses

Blue: AI graduate courses that replace Pharm.D. electives

Green: Additional AI courses required for the M.S. degree

NOTE: 9 credits are included within the Pharm.D. curriculum; shared credit degree students will need to take and pay for an additional 21 credits.