Term Schedule
Only courses with a DSC course number are listed on this page. See MS program for all of the required and elective courses for the degree.
Fall 2017
| Number | Title | Instructor | Time |
|---|
|
DSC 401
MORT B
MW 9:00AM - 10:15AM
|
|
This course provides a hands-on introduction to widely-used tools for data science. Topics include Linux; languages and packages for statistical analysis and visualization; cluster and parallel computing using Hadoop and Spark; libraries for machine learning; no-sql data stores; and cloud services. BUILDING: HARK | ROOM: 210 |
|
DSC 462 (DSC 262)
ALMUDEVAR A
TR 2:00PM - 3:15PM
|
|
This course will cover foundational concepts in probability and statistical inference, with an emphasis on topics of interest to computer scientists. Following an introduction to elementary probability theory, topics will include applications of combinatorics; Markov chains; principles of statistical classification (Bayes' rule, sensitivity and specificity, ROC curves) and random number generation. The theory of statistical estimation and hypothesis testing will be introduced, and applied to one and two sample inference for population means, proportions, variances and correlations. Nonparametric procedures will be discussed. Topics also include statistical modeling (ANOVA, simple and multiple regression), and computational methods. Students will be introduced to the R statistical computing environment. BUILDING: WEGMN | ROOM: 1400 PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171 |
|
DSC 491
–
–
|
|
No description BUILDING: | ROOM: |
|
DSC 530 (DSC 530)
KAUTZ H
MW 10:25AM - 11:40AM
|
|
This course provides a hand-on introduction to experimental and analytical methods in cognitive science and artificial intelligence. Each year, it offers three modules from a rotating list, including topics such as brain imaging, computational linguistics, and computer vision. The course is open to graduate students in any discipline. The course is recommended for who intend to pursue research in the the intersection of cognitive science and computer science, but prior experience in those fields is not required. It is required for students supported by the BCS/CS NRT graduate training grant. The modules are on Bayesian neuroscience, computational models in cognitive science, and natural language dialog systems. BUILDING: MEL | ROOM: 269 PREREQUISITES: none |
|
DSC 895
–
–
|
|
No description BUILDING: | ROOM: |
|
DSC 985
–
–
|
|
No description BUILDING: | ROOM: |
Fall 2017
| Number | Title | Instructor | Time |
|---|---|
| Monday and Wednesday | |
|
DSC 401
MORT B
MW 9:00AM - 10:15AM
|
|
|
This course provides a hands-on introduction to widely-used tools for data science. Topics include Linux; languages and packages for statistical analysis and visualization; cluster and parallel computing using Hadoop and Spark; libraries for machine learning; no-sql data stores; and cloud services. BUILDING: HARK | ROOM: 210 |
|
|
DSC 530 (DSC 530)
KAUTZ H
MW 10:25AM - 11:40AM
|
|
|
This course provides a hand-on introduction to experimental and analytical methods in cognitive science and artificial intelligence. Each year, it offers three modules from a rotating list, including topics such as brain imaging, computational linguistics, and computer vision. The course is open to graduate students in any discipline. The course is recommended for who intend to pursue research in the the intersection of cognitive science and computer science, but prior experience in those fields is not required. It is required for students supported by the BCS/CS NRT graduate training grant. The modules are on Bayesian neuroscience, computational models in cognitive science, and natural language dialog systems. BUILDING: MEL | ROOM: 269 PREREQUISITES: none |
|
| Tuesday and Thursday | |
|
DSC 462 (DSC 262)
ALMUDEVAR A
TR 2:00PM - 3:15PM
|
|
|
This course will cover foundational concepts in probability and statistical inference, with an emphasis on topics of interest to computer scientists. Following an introduction to elementary probability theory, topics will include applications of combinatorics; Markov chains; principles of statistical classification (Bayes' rule, sensitivity and specificity, ROC curves) and random number generation. The theory of statistical estimation and hypothesis testing will be introduced, and applied to one and two sample inference for population means, proportions, variances and correlations. Nonparametric procedures will be discussed. Topics also include statistical modeling (ANOVA, simple and multiple regression), and computational methods. Students will be introduced to the R statistical computing environment. BUILDING: WEGMN | ROOM: 1400 PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171 |
|
| TBA | |
|
DSC 491
–
–
|
|
|
No description BUILDING: | ROOM: |
|
|
DSC 895
–
–
|
|
|
No description BUILDING: | ROOM: |
|
|
DSC 985
–
–
|
|
|
No description BUILDING: | ROOM: |
|