PHYS3358 - Data Analysis for the Natural Sciences I: Fundamentals

Status
A
Activity
LEC
Section number integer
1
Title (text only)
Data Analysis for the Natural Sciences I: Fundamentals
Term
2024C
Subject area
PHYS
Section number only
001
Section ID
PHYS3358001
Course number integer
3358
Meeting times
TR 1:45 PM-3:14 PM
Level
undergraduate
Instructors
Arnold Mathijssen
Description
This is a course on the fundamentals of data analysis and statistical inference for the natural sciences. Topics include probability distributions, linear and non-linear regression, Monte Carlo methods, frequentist and Bayesian data analysis, parameter and error estimation, Fourier analysis, power spectra, and signal and image analysis techniques. Students will obtain both the theoretical background in data analysis and also get hands-on experience analyzing real scientific data. Prerequisite: Prior programming experience.
Course number only
3358
Fulfills
Natural Sciences & Mathematics Sector
Use local description
No

PHYS3314 - Ocean-Atmosphere Dynamics and Implications for Future Climate Change

Status
A
Activity
REC
Section number integer
402
Title (text only)
Ocean-Atmosphere Dynamics and Implications for Future Climate Change
Term
2024C
Subject area
PHYS
Section number only
402
Section ID
PHYS3314402
Course number integer
3314
Meeting times
F 1:45 PM-2:44 PM
Level
undergraduate
Instructors
Irina Marinov
Description
This course covers the fundamentals of atmosphere and ocean dynamics, and aims to put these in the context of climate change in the 21st century. Large-scale atmospheric and oceanic circulation, the global energy balance, and the global energy balance, and the global hydrological cycle. We will introduce concepts of fluid dynamics and we will apply these to the vertical and horizontal motions in the atmosphere and ocean. Concepts covered include: hydrostatic law, buoyancy and convection, basic equations of fluid motions, Hadley and Ferrel cells in the atmosphere, thermohaline circulation, Sverdrup ocean flow, modes of climate variability (El-Nino, North Atlantic Oscillation, Southern Annular Mode). The course will incorporate student led discussions based on readings of the 2007 Intergovernmental Panel on Climate Change (IPCC) report and recent literature on climate change. Aimed at undergraduate or graduate students who have no prior knowledge of meteorology or oceanography or training in fluid mechanics. Previous background in calculus and/or introductory physics is helpful. This is a general course which spans many subdisciplines (fluid mechanics, atmospheric science, oceanography, hydrology).
Course number only
3314
Cross listings
EESC4336402, EESC6336402
Use local description
No

PHYS3314 - Ocean-Atmosphere Dynamics and Implications for Future Climate Change

Status
A
Activity
LEC
Section number integer
401
Title (text only)
Ocean-Atmosphere Dynamics and Implications for Future Climate Change
Term
2024C
Subject area
PHYS
Section number only
401
Section ID
PHYS3314401
Course number integer
3314
Meeting times
MW 3:30 PM-4:59 PM
Level
undergraduate
Instructors
Irina Marinov
Description
This course covers the fundamentals of atmosphere and ocean dynamics, and aims to put these in the context of climate change in the 21st century. Large-scale atmospheric and oceanic circulation, the global energy balance, and the global energy balance, and the global hydrological cycle. We will introduce concepts of fluid dynamics and we will apply these to the vertical and horizontal motions in the atmosphere and ocean. Concepts covered include: hydrostatic law, buoyancy and convection, basic equations of fluid motions, Hadley and Ferrel cells in the atmosphere, thermohaline circulation, Sverdrup ocean flow, modes of climate variability (El-Nino, North Atlantic Oscillation, Southern Annular Mode). The course will incorporate student led discussions based on readings of the 2007 Intergovernmental Panel on Climate Change (IPCC) report and recent literature on climate change. Aimed at undergraduate or graduate students who have no prior knowledge of meteorology or oceanography or training in fluid mechanics. Previous background in calculus and/or introductory physics is helpful. This is a general course which spans many subdisciplines (fluid mechanics, atmospheric science, oceanography, hydrology).
Course number only
3314
Cross listings
EESC4336401, EESC6336401
Use local description
No

PHYS2280 - Physical Models of Biological Systems

Status
A
Activity
LEC
Section number integer
401
Title (text only)
Physical Models of Biological Systems
Term
2024C
Syllabus URL
Subject area
PHYS
Section number only
401
Section ID
PHYS2280401
Course number integer
2280
Meeting times
MW 1:45 PM-3:14 PM
Level
undergraduate
Instructors
Philip C Nelson
Description
Classic case studies of successful reductionistic models of complex phenomena, emphasizing the key steps of making estimates, using them to figure out which physical variables and phenomena will be most relevant to a given system, finding analogies to purely physical systems whose behavior is already known, and embodying those in a mathematical model, which is often implemented in computer code. Topics may include bacterial genetics, genetic switches and oscillators; systems that sense or utilize light; superresolution and other newmicroscopy methods; and vision and other modes of sensory transduction.
Course number only
2280
Cross listings
BCHE2280401
Fulfills
Natural Sciences & Mathematics Sector
Use local description
No

PHYS2200 - Applied Data Science - Deep Learning and Artificial Intelligence

Status
A
Activity
REC
Section number integer
205
Title (text only)
Applied Data Science - Deep Learning and Artificial Intelligence
Term
2024C
Syllabus URL
Subject area
PHYS
Section number only
205
Section ID
PHYS2200205
Course number integer
2200
Meeting times
F 3:30 PM-6:29 PM
Level
undergraduate
Instructors
Masao Sako
Description
This is the second of a two-semester gateway course on programming, data analysis, and data science in Python. This semester will focus on big data, machine learning, and artificial intelligence and we will dive deeper into the practical applications of these data science methodologies using real-world data. Topics covered include supervised and unsupervised machine learning, decision trees, random forests, neural networks, and deep learning. Some modern methods such as transformers and generative AI will also be discussed. Finally, we will explore effective ways of using AI chatbots such as ChatGPT for efficiently building software.
Course number only
2200
Use local description
No

PHYS2200 - Applied Data Science - Deep Learning and Artificial Intelligence

Status
A
Activity
REC
Section number integer
204
Title (text only)
Applied Data Science - Deep Learning and Artificial Intelligence
Term
2024C
Syllabus URL
Subject area
PHYS
Section number only
204
Section ID
PHYS2200204
Course number integer
2200
Meeting times
R 5:15 PM-8:14 PM
Level
undergraduate
Instructors
Masao Sako
Description
This is the second of a two-semester gateway course on programming, data analysis, and data science in Python. This semester will focus on big data, machine learning, and artificial intelligence and we will dive deeper into the practical applications of these data science methodologies using real-world data. Topics covered include supervised and unsupervised machine learning, decision trees, random forests, neural networks, and deep learning. Some modern methods such as transformers and generative AI will also be discussed. Finally, we will explore effective ways of using AI chatbots such as ChatGPT for efficiently building software.
Course number only
2200
Use local description
No

PHYS2200 - Applied Data Science - Deep Learning and Artificial Intelligence

Status
A
Activity
REC
Section number integer
203
Title (text only)
Applied Data Science - Deep Learning and Artificial Intelligence
Term
2024C
Syllabus URL
Subject area
PHYS
Section number only
203
Section ID
PHYS2200203
Course number integer
2200
Meeting times
W 5:15 PM-8:14 PM
Level
undergraduate
Instructors
Masao Sako
Description
This is the second of a two-semester gateway course on programming, data analysis, and data science in Python. This semester will focus on big data, machine learning, and artificial intelligence and we will dive deeper into the practical applications of these data science methodologies using real-world data. Topics covered include supervised and unsupervised machine learning, decision trees, random forests, neural networks, and deep learning. Some modern methods such as transformers and generative AI will also be discussed. Finally, we will explore effective ways of using AI chatbots such as ChatGPT for efficiently building software.
Course number only
2200
Use local description
No

PHYS2200 - Applied Data Science - Deep Learning and Artificial Intelligence

Status
A
Activity
REC
Section number integer
202
Title (text only)
Applied Data Science - Deep Learning and Artificial Intelligence
Term
2024C
Syllabus URL
Subject area
PHYS
Section number only
202
Section ID
PHYS2200202
Course number integer
2200
Meeting times
W 3:30 PM-6:29 PM
Level
undergraduate
Instructors
Masao Sako
Description
This is the second of a two-semester gateway course on programming, data analysis, and data science in Python. This semester will focus on big data, machine learning, and artificial intelligence and we will dive deeper into the practical applications of these data science methodologies using real-world data. Topics covered include supervised and unsupervised machine learning, decision trees, random forests, neural networks, and deep learning. Some modern methods such as transformers and generative AI will also be discussed. Finally, we will explore effective ways of using AI chatbots such as ChatGPT for efficiently building software.
Course number only
2200
Use local description
No

PHYS2200 - Applied Data Science - Deep Learning and Artificial Intelligence

Status
A
Activity
REC
Section number integer
201
Title (text only)
Applied Data Science - Deep Learning and Artificial Intelligence
Term
2024C
Syllabus URL
Subject area
PHYS
Section number only
201
Section ID
PHYS2200201
Course number integer
2200
Meeting times
M 3:30 PM-6:29 PM
Level
undergraduate
Instructors
Masao Sako
Description
This is the second of a two-semester gateway course on programming, data analysis, and data science in Python. This semester will focus on big data, machine learning, and artificial intelligence and we will dive deeper into the practical applications of these data science methodologies using real-world data. Topics covered include supervised and unsupervised machine learning, decision trees, random forests, neural networks, and deep learning. Some modern methods such as transformers and generative AI will also be discussed. Finally, we will explore effective ways of using AI chatbots such as ChatGPT for efficiently building software.
Course number only
2200
Use local description
No

PHYS2200 - Applied Data Science - Deep Learning and Artificial Intelligence

Status
A
Activity
LEC
Section number integer
1
Title (text only)
Applied Data Science - Deep Learning and Artificial Intelligence
Term
2024C
Syllabus URL
Subject area
PHYS
Section number only
001
Section ID
PHYS2200001
Course number integer
2200
Meeting times
TR 1:45 PM-3:14 PM
Level
undergraduate
Instructors
Masao Sako
Description
This is the second of a two-semester gateway course on programming, data analysis, and data science in Python. This semester will focus on big data, machine learning, and artificial intelligence and we will dive deeper into the practical applications of these data science methodologies using real-world data. Topics covered include supervised and unsupervised machine learning, decision trees, random forests, neural networks, and deep learning. Some modern methods such as transformers and generative AI will also be discussed. Finally, we will explore effective ways of using AI chatbots such as ChatGPT for efficiently building software.
Course number only
2200
Use local description
No