FRM Part I Changes in 2023 with respect to 2022  An Overview
The FRM 2023 curriculum comes up with a few changes in Learning objectives and the addition of two new chapters.
The following changes have been made in the second module  Quantitative Analysis (QTA) 
In QTA 7  Linear Regression, a new learning objective is added 

Estimate the correlation coefficient from the R measure obtained in linear regressions with a single explanatory variable.
In QTA 8  Regression with Multiple Explanatory variables, a new learning objective has been added 

Calculate the regression R2 using the three components of the decomposed variation of the dependent variable data: the explained sum of squares, the total sum of squares and residual sum of squares.
In QTA 12  Measuring Returns, Volatility & Correlations, a new Learning objective is added 

Compare and contrast the different measures of correlation used to assess dependence.
It must be noted that the content is same as the 2022 curriculum for all these chapters, and hence the concepts related to these new learning objectives had already been covered in the previous lecture videos as well as our study material. The addition of these learning objectives has made these concepts more likely to get tested as compared to the previous year..
Two new readings have been added in Quantitative Analysis 
In QTA 14  Machine Learning Methods 
This reading is an introduction to the basics of Machine learning and its relevance in the area of Financial Risk Management. The following learning objectives are a part of this reading 

Discuss the philosophical and practical differences between machinelearning techniques and classical econometrics.

Explain the differences among the training, validation, and test data subsamples, and how each is used.

Understand the differences between and consequences of underfitting and overfitting, and propose potential remedies for each.

Use principal components analysis to reduce the dimensionality of a set of features.

Describe how the Kmeans algorithm separates a sample into clusters.

Be aware of natural language processing and how it is used.

Differentiate among unsupervised, supervised, and reinforcement learning models.

Explain how reinforcement learning operates and how it is used in decisionmaking.
In QTA 15  Machine learning and prediction 
This reading deals with categorical variables and is more related to predictive abilities of machine learning models. The following learning objectives are a part of this reading 

Explain the role of linear regression and logistic regression in prediction.

Understand how to encode categorical variables.

Discuss why regularization is useful, and distinguish between the ridge regression and LASSO approaches.

Show how a decision tree is constructed and interpreted.

Describe how ensembles of learners are built.

Outline the intuition behind the K nearest neighbors and support vector machine methods for classification.

Understand how neural networks are constructed and how their weights are determined.

Evaluate the predictive performance of logistic regression models and neural network models using a confusion matrix.
For these two new readings, the learning objectives mostly emphasize on a more conceptual and theoretical construct rather than a calculative one. Numerical segments are fewer in number.
One minor change has been made in the fourth module  Valuation and Risk Models (VRM) 
In VRM 16  Option Sensitivity Measures: The Greeks, one learning objective has been modified as follows 

Old LO  Describe delta hedging for an option, forward, and futures contracts.

New LO  Describe delta hedging for an option.
Here also, the content has not changed with respect to 2022.