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Location & Time: E213 Westgate, Tuesday and Thursday 10:35 AM to 11:50 AM

The overarching goal of this course is to impress upon students the enormous potential of Artificial Intelligence (AI) to be used as an agent for good in today’s increasingly connected societies. To achieve this goal, this course explores a set of advanced AI methods such as machine learning, convex and combinatorial optimization, game theory and mechanism design, sequential planning, etc., and illustrates how these methods have been used to tackle challenging problems that afflict humanity, particularly in the areas of healthcare, conservation and public safety and security. The intended audience for this course are PhD students, Masters students, and advanced undergraduates interested in exploring research questions in AI which lead to a tangible societal impact. To get the most out of this course, the student should have a decent mathematical background, although that is not required and will not be used a criteria for course evaluation. Although the course is listed with IST, graduate students in CS, EE, OR and the Social Work departments would also find this course of interest.

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Location & Time: Earth and Eng Sciences 121, Tuesday and Thursday 4:35 PM to 5:50 PM

The field of Machine Learning (ML) has seen spectacular successes over the last couple of decades in a myriad of domains. The popularity of ML can be gauged by the increasingly blurred boundaries between AI and ML within Silicon Valley offices. The overarching goal of this course is to exposure students to new emerging issues and technologies in the field of Machine Learning. This course also aims to provide hands-on experience in studying recent ML advances in detail. To achieve this goal, this course begins by charting the historical underpinnings of the field of Machine Learning in order to provide the students with an understanding of how the field evolved to its current state. Next, we explore new and emerging areas in ML including (i) Big Data and the Rise of Deep Learning; (ii) FAT-ML (Fairness, Accountability, Transparency in Machine Learning); (iii) Adversarial ML; (iv) Ethics in AI and ML. The discussion in each of these topics will be motivated by real-world case studies which will provide the students with a hands-on understanding and workable knowledge of these topics. The intended audience for this course are undergraduate students interested in learning about the most important future directions that the field of ML will take.

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Location & Time: Westgate 206, Tuesday and Thursday 1:35 PM to 2:50 PM

This course provides an overview of the foundations, problems, approaches, implementation, and applications of, artificial intelligence. Topics covered include problem solving, goal-based and adversarial search, logical, probabilistic, and decision theoretic knowledge representation and inference, decision making, and learning. Through programming assignments that sample these topics, students acquire an understanding of what it means to build rational agents of different sorts as well as applications of AI techniques in language processing, planning, vision.

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Location & Time: Westgate 206, Monday, Wednesday and Friday 9:05 AM to 9:55 AM

The course will introduce students to the principles of data mining and machine learning, representative learning algorithms, and their applications in data sciences. Topics to be covered include principled approaches to supervised learning, principled approaches to unsupervised learning, feature engineering, dimensionality reduction, performance assessment of models, and relative strengths and weaknesses of alternative algorithms.

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Location & Time: Nittany Lion Inn Boardroom 102, Tuesday and Thursday 9:05 AM to 10:20 AM

This course provides an overview of the foundations, problems, approaches, implementation, and applications of, artificial intelligence. Topics covered include problem solving, goal-based and adversarial search, logical, probabilistic, and decision theoretic knowledge representation and inference, decision making, and learning. Through programming assignments that sample these topics, students acquire an understanding of what it means to build rational agents of different sorts as well as applications of AI techniques in language processing, planning, vision.

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Location & Time: Westgate W201, Wednesday 6:00 PM to 9:00 PM

The course will introduce students to the principles of data mining and machine learning, representative learning algorithms, and their applications in data sciences. Topics to be covered include principled approaches to supervised learning, principled approaches to unsupervised learning, feature engineering, dimensionality reduction, performance assessment of models, and relative strengths and weaknesses of alternative algorithms.

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Location & Time: Westgate E202, Tuesday/Thursday 9:00 AM to 10:30 AM

The course will introduce students to the principles of data mining and machine learning, representative learning algorithms, and their applications in data sciences. Topics to be covered include principled approaches to supervised learning, principled approaches to unsupervised learning, feature engineering, dimensionality reduction, performance assessment of models, and relative strengths and weaknesses of alternative algorithms.

Course Webpage

Location & Time: Westgate E202, Tuesday/Thursday 9:00 AM to 10:30 AM

This course provides an overview of the foundations, problems, approaches, implementation, and applications of, artificial intelligence. Topics covered include problem solving, goal-based and adversarial search, logical, probabilistic, and decision theoretic knowledge representation and inference, decision making, and learning. Through programming assignments that sample these topics, students acquire an understanding of what it means to build rational agents of different sorts as well as applications of AI techniques in language processing, planning, vision.

Course Webpage

Location & Time: Westgate E213, Wednesday/Friday 10:45 AM to 12:00 PM

The overarching goal of this course is to impress upon students the enormous potential of Artificial Intelligence (AI) to be used as an agent for good in today’s increasingly connected societies. To achieve this goal, this course explores a set of advanced AI methods such as machine learning, convex and combinatorial optimization, game theory and mechanism design, sequential planning, etc., and illustrates how these methods have been used to tackle challenging problems that afflict humanity, particularly in the areas of healthcare, conservation and public safety and security. The intended audience for this course are PhD students, Masters students, and advanced undergraduates interested in exploring research questions in AI which lead to a tangible societal impact. To get the most out of this course, the student should have a decent mathematical background, although that is not required and will not be used a criteria for course evaluation. Although the course is listed with IST, graduate students in CS, EE, OR and the Social Work departments would also find this course of interest.