|
週次 |
日期 |
單元主題 |
|
第1週 |
9/28 |
meet.google.com/kjb-qxer-rna
1. Introduction of the course
2. The basic of Python, Jupyter Notebook, github, google colab
3. The importance of data visualization |
|
第2週 |
10/05 |
meet.google.com/kjb-qxer-rna
Data analysis
1. Research concepts / scientific method
2. Motivation of data analysis in astronomy
3. A short introduction of astronomical surveys and simulations
Astronomy
1. Introduction to some results in astronomy and the analysis methods used in those studies
2. SDSS |
|
第3週 |
10/12 |
meet.google.com/kjb-qxer-rna
Data analysis
1. Data exploration
2. basic statistics / uncertainty estimation / statistical estimators
Astronomy
1. Color-magnitude distribution of stars, galaxies, and quasars |
|
第4週 |
10/19 |
新物館 833上課
https://docs.google.com/document/d/18DF3hOO4PNEtOdnmE_9CmpwG8qjwZJz5_ig2Bj-1xmM/edit?usp=sharing
Data analysis
1. Correlation coefficients
2. Spatial correlation in astronomy
3. Cross-correlation measurements in astronomy
Astronomy
1. Milky Way All Sky map exploration
2. Weak lensing probing dark matter halos
3. Correlation function / BAO
4. SZ effect / hot gas |
|
第5週 |
10/26 |
Data analysis
1. Regression
Astronomy
1. Type 1a Supernova probing dark energy
2. Galaxy rotation curve
3. Estimating emission line / absorption line strength / Gas abundance
4. Galaxy stellar mass - SFR relation |
|
第6週 |
11/02 |
Data analysis
1. searching for structure in data
2. smoothing
3. object cross-matching
(4. Decadal Survey discussion TMP)
Astronomy
1. Stellar streams of Milky Way
2. Obscured quasars
3. Multi-wavelength studies in astronomy |
|
第7週 |
11/09 |
Data analysis
1. Midterm presentation
|
|
第8週 |
11/16 |
Data analysis
1.The basic of machine learning
2. Dimensionality reduction techniques
Astronomy
1. Intergalatic medium / Reionization
2. Direct imaging of exoplanets
3. Intercluster medium |
|
第9週 |
11/23 |
Data analysis
1. Dimensionality reduction techniques
Astronomy
1. Intergalatic medium / Reionization
2. Direct imaging of exoplanets
3. Intercluster medium |
|
第10週 |
11/30 |
Data analysis
1. Support Vector Machine
2. Logistic regression
3. Decision tree
4. Random forest
Astronomy
1. Photometric redshifts
2. Target selection of surveys |
|
第11週 |
12/07 |
Data analysis
1. Support Vector Machine
2. Logistic regression
3. Decision tree
4. Random forest
Astronomy
1. Photometric redshifts
2. Target selection of surveys |
|
第12週 |
12/14 |
Data analysis
1. Neural network / Deep learning
2. Ethic of machine learning
Astronomy
1. Real world application / object detection / Face recognition |
|
第13週 |
12/21 |
Data analysis
1. Convolutional neural network
Astronomy
1. Galaxy classification
2. Absorption line detection
3. Supernova candidate detection
4. Dark matter substructure
5. Gravitational lensing |
|
第14週 |
12/28 |
Data analysis
1. Convolutional neural network
Astronomy
1. Galaxy classification
2. Absorption line detection
3. Supernova candidate detection
4. Dark matter substructure
5. Gravitational lensing |
|
第15週 |
1/04 |
1. Final presentation |
|
第16週 |
1/11 |
1. Final presentation
2. Summary |