More calendar

_modules/module-1.md

@@ -29,30 +29,30 @@ Sep/20
 : **Student Holiday!**{: .label .label-gray} No recitation
 
 Sep/24
-: **Lecture 6**{: .label .label-red}
+: **Lecture 6**{: .label .label-red} Bias/variance, regularization 
 
 Sep/26
-: **Lecture 7**{: .label .label-red}
+: **Lecture 7**{: .label .label-red} Evaluating estimators, consistency
 
 Sep/27
 : **Recitation**{: .label .label-purple}
 
 Oct/1
-: **Lecture 8**{: .label .label-red}
+: **Lecture 8**{: .label .label-red} Classification, logistic regression <br>
 HW2 due; HW3 out
 
 Oct/3
-: **Lecture 9**{: .label .label-red}
+: **Lecture 9**{: .label .label-red} Trees, forests, nearest-neighbor
 
 Oct/4
 : **Recitation**{: .label .label-purple}
 
 Oct/8
-: **Lecture 10**{: .label .label-red}
+: **Lecture 10**{: .label .label-red} Stochastic gradient descent <br>
 HW3 due; MP1 out
 
 Oct/10
-: **Lecture 11**{: .label .label-red}
+: **Lecture 11**{: .label .label-red} Neural networks
 
 Oct/11
 : **Recitation**{: .label .label-purple}
@@ -61,14 +61,14 @@ Oct/15
 : **Holiday!**{: .label .label-gray} No lecture
 
 Oct/17
-: **Lecture 12**{: .label .label-red}
+: **Lecture 12**{: .label .label-red} Scaling up neural networks <br>
 MP1 due
 
 Oct/18
 : **Review sessions**{: .label .label-blue}
 
 Oct/22
-: **Lecture 13**{: .label .label-red}
+: **Lecture 13**{: .label .label-red} Structured neural networks <br>
 HW4 out
 
 Oct/24
@@ -78,46 +78,46 @@ Oct/25
 : **No recitation**{: .label .label-gray}
 
 Oct/29
-: **Lecture 14**{: .label .label-red}
+: **Lecture 14**{: .label .label-red} Making guarantees 
 
 Oct/31
-: **Lecture 15**{: .label .label-red}
+: **Lecture 15**{: .label .label-red} Uncertainty quantification
 
 Nov/1
 : **Recitation**{: .label .label-purple}
 
 Nov/5
-: **Lecture 16**{: .label .label-red}
+: **Lecture 16**{: .label .label-red} Modeling densities <br>
 HW4 due; HW5 out
 
 Nov/7
-: **Lecture 17**{: .label .label-red}
+: **Lecture 17**{: .label .label-red} Principal components analysis
 
 Nov/8
 : **Recitation**{: .label .label-purple}
 
 Nov/12
-: **Lecture 18**{: .label .label-red}
+: **Lecture 18**{: .label .label-red} Handling missing data <br>
 HW5 due; HW6 out
 
 Nov/14
-: **Lecture 19**{: .label .label-red}
+: **Lecture 19**{: .label .label-red} Temporal and spatial data
 
 Nov/15
 : **Recitation**{: .label .label-purple}
 
 Nov/19
-: **Lecture 20**{: .label .label-red}
+: **Lecture 20**{: .label .label-red} Graphical models <br>
 HW6 due; MP2 out
 
 Nov/21
-: **Lecture 21**{: .label .label-red}
+: **Lecture 21**{: .label .label-red} Modeling complex densities
 
 Nov/22
 : **Recitation**{: .label .label-purple}
 
 Nov/26
-: **Lecture 22**{: .label .label-red}
+: **Lecture 22**{: .label .label-red} Diffusion and flow models
 
 Nov/28
 : **Holiday!**{: .label .label-gray} No lecture
@@ -126,10 +126,10 @@ Nov/15
 : **Holiday!**{: .label .label-gray} No recitation
 
 Dec/3
-: **Lecture 23**{: .label .label-red}
+: **Lecture 23**{: .label .label-red} Working with foundation models
 
 Dec/5
-: **Lecture 24**{: .label .label-red} 
+: **Lecture 24**{: .label .label-red} Applications in small-data regime
 
 Dec/6
 : **Recitation**{: .label .label-purple} 

info/calendar.md

@@ -9,20 +9,25 @@ nav_order: 0
 # Calendar
 
 {: .warning}
+Topics are subject to update.
 
-Rough semester calendar draft; dates/events are subject to change/update.
 {% for module in site.modules %}
 {{ module }}
 {% endfor %}
 
+# Primary textbooks
+- [Probabilistic Machine Learning: An Introduction](https://probml.github.io/pml-book/book1.html), Kevin Murphy, MIT Press, 2022.
+- [Probabilistic Machine Learning: Advanced Topics](https://probml.github.io/pml-book/book2.html), Kevin Murphy, MIT Press, 2023.
 
-# Recommended Reading
-
-All freely accessible (an MIT IP may be required):
+# Other recommended reading
 
 - [B] [Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf), Bishop; Springer, 2006.
 - [HTF] [The Elements of Statistical Learning](https://hastie.su.domains/ElemStatLearn/), Hastie, Tibshirani, and Friedman; Springer, 2009.
 - [SB]/[SSS] [Understanding Machine Learning: From Theory to Algorithms](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning), Shalev-Shwartz and Ben-David; Cambridge University Press, 2014.
-<!-- - [SB] [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2020trimmed.pdf), Sutton and Barton; The MIT Press, 2018. -->
 - [JWHT] [An Introduction to Statistical Learning](https://www.statlearning.com/), James, Witten, Hastie, and Tibshirani; Springer, 2013.
-<!-- - [BV] [Convex Optimization](https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf), Boyd and Vandenberghe; Cambridge University Press, 2004 -->
+
+All freely accessible (an MIT IP may be required)
+
+
+
+